# Event-based Vision Resources

## <a href="https://sites.google.com/view/iros-2024-workshop">IROS 2024. Workshop on Embodied Neuromorphic AI for Robotic Perception and Control, Oct 14 (PM), 2024.</a>
## <a href="https://neural-fields-beyond-cams.github.io/">ECCV 2024. 1st Workshop on Neural Fields Beyond Conventional Cameras, Sep 30 (AM), 2024.</a>
## <a href="https://sites.google.com/view/nevi2024">ECCV 2024. Workshop on Neuromorphic Vision (NeVi), Sep 29 (PM), 2024.</a>
## <a href="https://tub-rip.github.io/eventvision2023/">CVPR 2023 Workshop on Event-based Vision, June 19, 2023.</a>
## <a href="https://www.neuropac.info/">NeuroPAC</a>

## Table of Contents:
- [Survey paper](#survey_paper)
- [Workshops](#workshops)
- [Devices and Manufacturers](#devices)
- [Companies working on Event-based Vision](#companies_sftwr)
- [Neuromorphic Systems](#neuromorphic-systems)
- [Review papers](#reviewpapers)
    - [Bio-inspiration](#reviewpapers-bio)
    - [Algorithms, Applications](#reviewpapers-algs)

- [Applications / Algorithms](#algorithms)
    - [Feature Detection and Tracking](#feature-detection)
        - [Corners](#corner-detection)
        - [Particles in fluids](#particle-detection)
        - [Eye Tracking](#eye_tracking)
    - [Optical Flow Estimation](#optical-flow-estimation)
        - [Scene Flow Estimation](#scene-flow-estimation)
    - [Reconstruction of Visual Information](#visualization)
        - [Intensity-Image Reconstruction](#image-reconstruction)
        - [Video Synthesis](#video-synthesis)
        - [Image super-resolution](#super-resolution)
        - [Joint/guided filtering](#joint-filtering)
        - [Tone mapping](#tone-mapping)
        - [Visual Stabilization](#visual-stabilization)
        - [Polarization Reconstruction](@polarization-reconstruction)
    - [Depth Estimation (3D Reconstruction)](#depth-estimation)
        - [Monocular](#depth-mono)
        - [Stereo](#depth-stereo)
        - [Stereoscopic panoramic imaging](#depth-stereo-pano)
    - [SLAM (Simultaneous Localization And Mapping)](#slam)
        - [Localization, Ego-motion estimation](#slam-localization)
        - [Visual Odometry](#visual-odometry)
        - [Visual-Inertial Odometry](#visual-inertial)
    - [Segmentation](#segmentation)
        - [Object Segmentation](#object-segmentation)
        - [Motion Segmentation](#motion-segmentation)
    - [Pattern recognition](#pattern-recognition)
        - [Object Recognition](#object-recognition)
        - [Gesture Recognition](#gesture-recognition)
        - [Representation / Feature Extraction](#learning-representation-features)
        - [Regression Tasks](#learning-regression)
        - [Learning Methods / Frameworks](#learning-methods-frameworks)
    - [Signal Processing](#signal_processing)
        - [Event Denoising](#denoising)
        - [Compression](#compression)
    - [Control](#control)
    - [Obstacle Avoidance](#obstacle_avoidance)
    - [Space Applications](#space)
    - [Tactile Sensing Applications](#tactile_sensing)
    - [Object Pose Estimation](#object_pose_estimation)
        - [Human Pose Estimation](#human_pose_estimation)
        - [Hand Pose Estimation](#hand_pose_estimation)
    - [Indoor Lighting Estimation](#indoor_lighting_estimation)
    - [Data Encryption](#data_encription)
    - [Nuclear Verification](#nuclear_verification)
    - [Optical Communication](#optical_communication)
    - [Animal Behavior Monitoring](#animal_monitoring)
    - [Optical Applications](#optical_applications)
      - [Auto-focus](#auto_focus)
      - [Speckle Analysis](#speckle_analysis)
      - [Interferometry or Holography](#interferometry_or_holography)
      - [Wavefront sensing](#wavefront_sensing)
      - [Optical super-resolution](#super_resolution_imaging)
      - [Schlieren imaging](#schlieren_imaging)
    - [Driver Monitoring System](#driver_monitoring_system)
      - [Multi-tasking networks: Face, Head Pose & Eye Gaze estimation](#DMS)
      - [Drowsiness or Yawn](#Drwosiness_or_yawn)
      - [Distraction](#distraction_detecton)
    - [Face Alignment and Landmark Detection](#face-alignment-landmarking)
    - [Visual Voice Activity Detection](#voice-activity-detection)

- [Simulators and Emulators](#simulators-emulators)
- [Datasets](#datasets)
- [Software](#software)
    - [Drivers](#drivers)
    - [Synchronization](#synchronization)
    - [Lens Calibration](#calibration)
    - [Algorithms](#software-algorithms)
    - [Utilities](#software-utilities)

- [Neuromorphic Processors and Platforms](#processors-platforms)
- [Courses](#teaching)
- [Theses and Dissertations](#theses)
    - [Dissertations](#theses-phd)
    - [Master's Theses](#theses-master)
- [People / Organizations](#people)
- [EETimes articles](#press-eetimes)
- [Contributing](#contributing)

___
<br>

<a name="survey_paper"></a>
# Survey paper
- <a name="Gallego20tpami"></a>Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A., Conradt, J., Daniilidis, K., Scaramuzza, D.,  
**_[Event-based Vision: A Survey](http://rpg.ifi.uzh.ch/docs/EventVisionSurvey.pdf)_**,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 44(1):154-180, Jan. 2022.


<a name="workshops"></a>
# Workshops
- [CVPR 2023 Fourth International Workshop on Event-based Vision](https://tub-rip.github.io/eventvision2023), **[Videos](https://www.youtube.com/playlist?list=PLeXWz-g2If96iotpzgBNNTr9VA6hG-LLK)**
- [IEEE Embedded Vision Workshop Series](https://embeddedvisionworkshop.wordpress.com), with focus on Biologically-inspired vision and embedded systems.
- [IISW 2023 Int. Image Sensor Workshop](https://imagesensors.org/2023-international-image-sensor-workshop/)
- [MFI 2022 First Neuromorphic Event Sensor Fusion Workshop](https://sites.google.com/view/eventsensorfusion2022/home) with videos incl. _Event Sensor Fusion Jeopardy_ game - Virtual. **[Videos](https://youtube.com/playlist?list=PLVtZ8f-q0U5gXhjN4inwWZi66bp5vp-lN)**
- [tinyML Neuromorphic Engineering Forum](https://www.tinyml.org/event/tinyml-neuromorphic-engineering-forum/) - Virtual, 2022. **[Videos](https://www.youtube.com/playlist?list=PLeisuBi-nfBM5HayCqF4KMBaJciV5UkLX)**
- [CVPR 2021 Third International Workshop on Event-based Vision](https://tub-rip.github.io/eventvision2021) - Virtual. **[Videos](https://www.youtube.com/playlist?list=PLeXWz-g2If95mjNpA-y-WIoDaoB8WtmE7)**
- [ICRA 2020 Workshop on Unconventional Sensors in Robotics](https://sites.google.com/view/unconventional-sensors) - Virtual. **[Videos](https://www.youtube.com/playlist?list=PLtW5yHT6tQuD4sLzkldzZEyQ4hz77K64-)**
- [Neuro-Inspired Computational Elements (NICE) Workshop Series](https://niceworkshop.org/nice-2019/). **[Videos](https://www.youtube.com/channel/UCKTLpjY9e8cMK12d2-Z-usA)**
- [Capo Caccia Workshops toward Cognitive Neuromorphic Engineering](http://capocaccia.iniforum.ch/).
- [The Telluride Neuromorphic Cognition Engineering Workshops](https://tellurideneuromorhic.org). **[Videos](https://sites.google.com/view/telluride2020/about-workshop/videos)**, Telluride 2020 (Online): **[Videos](https://www.youtube.com/playlist?list=PLG-iqBTOyCO5NAbqbsHPPnL9h35z0ooSE)**, **[Slides](https://drive.google.com/drive/folders/1lmUSjZoDb7yc_HO9xw0M5J4fIGRIfu_u)**
- [CVPR 2019 Second International Workshop on Event-based Vision and Smart Cameras](http://rpg.ifi.uzh.ch/CVPR19_event_vision_workshop.html). **[Videos](https://www.youtube.com/playlist?list=PLeXWz-g2If97iGiuBHmnW8IFIxwvSeCHx)**
- [IROS 2018 Unconventional Sensing and Processing for Robotic Visual Perception](https://www.jmartel.net/irosws-home).
- [ICRA 2017 First International Workshop on Event-based Vision](http://rpg.ifi.uzh.ch/ICRA17_event_vision_workshop.html). **[Videos](https://www.youtube.com/playlist?list=PLeXWz-g2If94k8mw6GcKU5C9PUgM1sK0U)**
- [IROS 2015 Event-Based Vision for High-Speed Robotics (slides)](http://www.rit.edu/kgcoe/iros15workshop/papers/IROS2015-WASRoP-Invited-04-slides.pdf), Workshop on Alternative Sensing for Robot Perception.
- [ICRA 2015 Workshop on Innovative Sensing for Robotics](http://innovative-sensing.mit.edu/), with a focus on Neuromorphic Sensors.
    

<a name="devices"></a>
# Devices & Companies Manufacturing them
- **DVS (Dynamic Vision Sensor)**: Lichtsteiner, P., Posch, C., and Delbruck, T., *[A 128x128 120dB 15μs latency asynchronous temporal contrast vision sensor](http://doi.org/10.1109/JSSC.2007.914337)*, IEEE J. Solid-State Circuits, 43(2):566-576, 2008. [PDF](https://www.ini.uzh.ch/~tobi/wiki/lib/exe/fetch.php?media=lichtsteiner_dvs_jssc08.pdf)
    - [Product page at iniVation](https://inivation.com/dvs/). [**Buy a DVS**](https://inivation.com/buy/)
    - [Product specifications](https://inivation.com/support/product-specifications/)    
    - [User guide](https://inivation.github.io/inivation-docs/Hardware%20user%20guides/User_guide_-_DVS128.html)
    - [Introductory videos about the DVS technology](https://inivation.com/dvs/videos/)
    - [iniVation AG](https://inivation.com/) invents, produces and sells neuromorphic technologies with a special focus on event-based vision into *business*. [Slides](http://rpg.ifi.uzh.ch/docs/ICRA17workshop/Jakobsen.pdf) by [S. E. Jakobsen](https://inivation.com/company/), board member of iniVation.
    - [Event Cameras - Tutorial - Tobi Delbruck, version 4](https://youtu.be/Th4TM4SsFGY)
- **Samsung's DVS**
    - [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Eric_Ryu_Samsung.pdf) and [Video](https://youtu.be/7fAPckjQSGE) by [Hyunsurk Eric Ryu](https://www.linkedin.com/in/hyunsurk-eric-ryu-82745712), Samsung Electronics  (2019).
    - Suh et al., *[A 1280×960 Dynamic Vision Sensor with a 4.95-μm Pixel Pitch and Motion Artifact Minimization](https://doi.org/10.1109/ISCAS45731.2020.9180436)*, IEEE Int. Symp. Circuits and Systems (ISCAS), 2020.    
    - Son, B., et al., *[A 640×480 dynamic vision sensor with a 9µm pixel and 300Meps address-event representation](https://doi.org/10.1109/ISSCC.2017.7870263)*, IEEE Int. Solid-State Circuits Conf. (ISSCC), 2017, pp. 66-67.
    - [SmartThings Vision](https://www.samsung.com/se/smartthings/smartthings-vision-u999/) commercial product for home monitoring. [in Australia](https://www.samsung.com/au/smart-home/smartthings-vision-u999/)
    - [Paper at IEDM 2019](#Park19iedm), about low-latency applications using Samsung's VGA DVS.
- **HVS** (Hybrid Vision Sensors) like  **ATIS**, **DAVIS**, **CDAVIS**, and other HVS that output brightness change events and intensity frames, either mono or color
    - **ATIS** [(Asynchronous Time-based Image Sensor), Posch et al. JSSC 2011](#Posch11jssc),
      *A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS*.
    - **DAVIS (Dynamic and Active Pixel Vision Sensor)**: Brandli, C., Berner, R., Yang, M., Liu, S.-C., Delbruck, T., *[A 240x180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor](https://doi.org/10.1109/JSSC.2014.2342715)*, IEEE J. Solid-State Circuits, 49(10):2333-2341, 2014. [PDF](https://drive.google.com/file/d/0BzvXOhBHjRhea3RrelA1V0RKVWM/view)
        - [Product page at iniVation](https://inivation.com/dvs/).  [**Buy a DAVIS**](https://inivation.com/buy/)
        - [Product specifications](https://inivation.com/support/product-specifications/)
        - [User guide](https://inivation.github.io/inivation-docs/Hardware%20user%20guides/User_guide_-_DAVIS240.html)
    - **DAVIS346**: Taverni, G; Paul Moeys, D; Li, C; Cavaco, C; Motsnyi, V; San Segundo Bello, D; Delbruck, T., *[Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison](http://dx.doi.org/10.1109/TCSII.2018.2824899)*, IEEE Trans. Circuits Syst. Express Briefs, 2018
    - **CDAVIS HVS**: Li, C., Brandli, C., Berner, R., Liu, H., Yang, M., Liu, S.-C., Delbruck, T., *[An RGBW color VGA rolling and global shutter dynamic and active-pixel vision sensor](https://www.imagesensors.org/Past%20Workshops/2015%20Workshop/2015%20Papers/Sessions/Session_13/13-05_Li_Delbruck.pdf)*, Int. Image Sensors Worskhop, 2015.
        - Prototype only 
    - **SDAVIS192**: Moeys, D. P., Corradi, F., Li, C., Bamford, S. A., Longinotti, L., Voigt, F. F., Berry, S., Taverni, G., Helmchen, F., Delbruck, T., *[A Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications](https://doi.org/10.1109/TBCAS.2017.2759783)*, IEEE Trans. Biomed. Circuits Syst. 12(1):123-136 2018.
        - Prototype only
    - **Omnivision HVS**: Guo et al, [A 3-Wafer-Stacked Hybrid 15MPixel CIS + 1 MPixel EVS with 4.6GEvent/s Readout, In-Pixel TDC and On-Chip ISP and ESP Function](http://dx.doi.org/10.1109/isscc42615.2023.10067476), ISSCC, (2023).
        - Prototype, commercially n.a. 
    - **Sony HVS**: Kodama et al., [1.22μm 35.6Mpixel RGB Hybrid Event-Based Vision Sensor with 4.88μm-Pitch Event Pixels and up to 10K Event Frame Rate by Adaptive Control on Event Sparsity](http://dx.doi.org/10.1109/ISSCC42615.2023.10067520), ISSCC (2023)
        - Prototype only, commercially n.a. 
- [**Insightness's Silicon Eye**](https://youtu.be/Y0mIb_MehK8) QVGA event sensor.
    - [The Silicon Eye Technology](http://www.insightness.com/?p=361)
    - [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Insightness.pdf) and [Video](https://youtu.be/9IJwF9xYEoU) by [Stefan Isler](http://www.insightness.com/#team) (2019).
    - [Slides](http://rpg.ifi.uzh.ch/docs/ICRA17workshop/Insightness.pdf) and [Video](https://youtu.be/6YyOW6DDGKw) by [Christian Brandli](http://www.insightness.com/#team), CEO and co-founder of Insightness (2017).
- [**PROPHESEE’s Metavision Sensor**](https://www.prophesee.ai/event-based-sensor-packaged/) and [**Software**](https://www.prophesee.ai/metavision-intelligence/)
    - **ATIS** <a name="Posch11jssc"></a> (Asynchronous Time-based Image Sensor): Posch, C., Matolin, D., Wohlgenannt, R. (2011). *[A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS](http://doi.org/10.1109/JSSC.2010.2085952)*, IEEE J. Solid-State Circuits, 46(1):259-275, 2011. [PDF](https://www.neuromorphic-vision.com/public/publications/2/publication.pdf), [YouTube](https://youtu.be/YQ8rT9Harb4), [YouTube](https://youtu.be/3Wiw8LA8hLs)
    - Prophesee Gen4 is described in: Finateu et al., *[A 1280×720 Back-Illuminated Stacked Temporal Contrast Event-Based Vision Sensor with 4.86μm Pixels, 1.066GEPS Readout, Programmable Event-Rate Controller and Compressive Data-Formatting Pipeline](https://doi.org/10.1109/ISSCC19947.2020.9063149)*, IEEE Int. Solid-State Circuits Conf. (ISSCC), 2020, pp. 112-114.
    - [**Buy a Prophesee packaged sensor VGA**](https://www.prophesee.ai/event-based-sensor-packaged)
    - [Prophesee Cameras Specifications](https://www.prophesee.ai/event-based-evaluation-kits/)
    - What is event-based vision and sample applications, [YouTube](https://www.youtube.com/watch?v=MjX3z-6n3iA)
    - [Download free or buy Metavision software ](https://www.prophesee.ai/metavision-intelligence/)
    - [Documentation and tutorials](https://docs.prophesee.ai/)
    - [Knowledge Base](https://support.prophesee.ai/portal/en/kb/prophesee-1) and [Community Forum](https://support.prophesee.ai/portal/en/community/forum)
- [**SONY's explanation of Event-based Vision Sensor (EVS) Technolgy**](https://www.sony-semicon.com/en/technology/industry/evs.html)
- [**CelePixel**](http://www.celepixel.com/), Shanghai. CeleX-V: the first 1 Mega-pixel event-camera sensor.
- **Sensitive DVS (sDVS)**
    - All are prototypes, commerically n.a.
    - Leñero-Bardallo, J. A., Serrano-Gotarredona, T., Linares-Barranco, B., *[A 3.6us Asynchronous Frame-Free Event-Driven Dynamic-Vision-Sensor](https://doi.org/10.1109/JSSC.2011.2118490)*,  IEEE J. of Solid-State Circuits, 46(6):1443-1455, 2011.
    - Serrano-Gotarredona, T. and Linares-Barranco, B., *[A 128x128 1.5% Contrast Sensitivity 0.9% FPN 3us Latency 4mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Amplifiers](https://doi.org/10.1109/JSSC.2012.2230553)*,  IEEE J. Solid-State Circuits, 48(3):827-838, 2013.
    - **SDAVIS192**: Moeys, D. P., Corradi, F., Li, C., Bamford, S. A., Longinotti, L., Voigt, F. F., Berry, S., Taverni, G., Helmchen, F., Delbruck, T., *[A Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications](https://doi.org/10.1109/TBCAS.2017.2759783)*, IEEE Trans. Biomed. Circuits Syst. 12(1):123-136 2018.
- **DLS (Dynamic Line Sensor)**: Posch, C., Hofstaetter, M., Matolin, D., Vanstraelen, G., Schoen, P., Donath, N., and Litzenberger, M., *[A dual-line optical transient sensor with on-chip precision time-stamp generation](https://doi.org/10.1109/ISSCC.2007.373513)*, IEEE Int. Solid-State Circuits Conf. - Digest of Technical Papers, Lisbon Falls, MN, US, 2007.
    - [Fact sheet at AIT](https://www.ait.ac.at/fileadmin/mc/digital_safety_security/downloads/Factsheet_-_Linescan-Chip_DLS_en.pdf).
- **LWIR DVS**: Posch, C., Matolin, D., Wohlgenannt, R., Maier, T., Litzenberger, M., *[A Microbolometer Asynchronous Dynamic Vision Sensor for LWIR](https://doi.org/10.1109/JSEN.2009.2020658)*, IEEE Sensors Journal, 9(6):654-664, 2009.
    - Prototype, commercially n.a.
- **Smart DVS (GAEP)**: Posch, C., Hoffstaetter, M., Schoen, P., *[A SPARC-compatible general purpose Address-Event processor with 20-bit 10ns-resolution asynchronous sensor data interface in 0.18um CMOS](https://doi.org/10.1109/ISCAS.2010.5537575)*, IEEE Int. Symp. Circuits and Systems (ISCAS), 2010.
    - Prototype, commercially n.a.
- **PDAVIS (Polarization Event Camera)**:
  - Prototype, commercially n.a.
  - [Bio-inspired Polarization Event Camera](http://arxiv.org/abs/2112.01933), arXiv [cs.CV] (2021) [PDAVIS video](https://drive.google.com/file/d/157mT8960m_QCm15i8HlB5SVyf45X_NUo/view?usp=sharing).
  - [PDAVIS: Bio-inspired Polarization Event Camera](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/html/Haessig_PDAVIS_Bio-Inspired_Polarization_Event_Camera_CVPRW_2023_paper.html). CVPR-W Proceedings (2023)
- **Center Surround Event Camera (CSDVS)**: Delbruck, T., Li, C., Graca, R. & Mcreynolds, B.,  
*[Utility and Feasibility of a Center Surround Event Camera](http://arxiv.org/abs/2202.13076)*  
arXiv [cs.CV] (2022) [CSDVS videos](https://sites.google.com/view/csdvs/home)
  - Proposed architecture.

<a name="companies_sftwr"></a>
# Companies working on Event-based Vision
- [iniVation AG](https://inivation.com/) invents, produces and sells neuromorphic vision sensors (DAVIS, DVExplorer, and others), with a focus on event-based vision for business; supplies the advanced [DV event camera software](https://docs.inivation.com/software/introduction.html).
- [iniLabs AG](https://inilabs.com/) invents neuromorphic technologies for *research*.
- [Samsung](http://www.samsung.com) develops Gen2 and Gen3 dynamic vision sensors and event-based vision solutions.
    - [IBM Research](http://www.research.ibm.com/articles/brain-chip.shtml) ([Synapse project](http://www.research.ibm.com/cognitive-computing/brainpower/)) and Samsung partenered to combine the [TrueNorth chip (brain) with a DVS (eye)](https://www.cnet.com/news/samsung-turns-ibms-brain-like-chip-into-a-digital-eye/).
- [Prophesee](http://www.prophesee.ai) (Formerly [Chronocam](http://www.chronocam.com/)) is the inventor and supplier of 4 Event-Based sensors generations, including commercial-grade versions as well as industry’s largest software suite. The company focuses on Industrial, Mobile-IoT and Automotive applications.
- [Insightness AG](http://www.insightness.com/) built visual systems to give mobile devices spatial awareness. [The Silicon Eye](http://www.insightness.com/?p=361) Technology. Aquired by Sony in 2019 and part of Sony Advanced Imager Sensors division.
- [SLAMcore](https://www.slamcore.com/) develops Localisation and mapping solutions for AR/VR, robotics & autonomous vehicles.
- [CelePixel](https://www.celepixel.com) (formerly Hillhouse Technology) offer integrated sensory platforms that incorporate various components and technologies, including a processing chipset and an image sensor (a dynamic vision sensor called CeleX).
- [AIT Austrian Institute of Technology](https://www.ait.ac.at/en/research-fields/new-sensor-technologies/optical-sensor-systems-for-industrial-processes/) sells neuromorphic sensor products.
    - [Inspection during production of carton packs](https://www.youtube.com/watch?v=8PZmb2z2bXw&index=39&list=PL659671AC92E70F19)
    - [UCOS Universal Counting Sensor](https://www.ait.ac.at/fileadmin/mc/digital_safety_security/downloads/Factsheet_-_People-Counting-Sensor_en.pdf)
    - [IVS Industrial Vision Sensor](https://www.ait.ac.at/fileadmin/mc/digital_safety_security/downloads/Factsheet_-_Industrial-Vision-Sensor_en.pdf)

<a name="neuromorphic-systems"></a>
# Neuromorphic Systems
- <a name="SerranoGotarredona99tcas"></a> Serrano-Gotarredona, T. , Andreou, A.G. , Linares-Barranco, B.,  
*[AER Image Filtering Architecture for Vision Processing Systems](https://doi.org/10.1109/81.788808)*,  
IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 46(9):1064-1071, 1999.
- <a name="SerranoGotarredona06anips"></a> Serrano-Gotarredona, R., Oster, M., Lichtsteiner, P., Linares-Barranco, A., Paz-Vicente, R., Gomez-Rodriguez, F., Riis, H.K., Delbruck, T., Liu, S.-H., Zahnd, S., Whatley, A.M., Douglas, R., Hafliger, P., Jimenez-Moreno, G., Civit, A.,  Serrano-Gotarredona, T., Acosta-Jimenez, A., Linares-Barranco, B.,  
*[AER building blocks for multi-layer multi-chip neuromorphic vision systems](http://papers.nips.cc/paper/2889-aer-building-blocks-for-multi-layer-multi-chip-neuromorphic-vision-systems.pdf)*,  
Advances in neural information processing systems, 1217-1224, 2006.
- <a name="Liu10conb"></a>Liu, S.-C. and Delbruck, T.,  
*[Neuromorphic sensory systems](https://doi.org/10.1016/j.conb.2010.03.007)*,  
Current Opinion in Neurobiology, 20:3(288-295), 2010.
- <a name="ZamarrenoRamos13tbcas"></a> Zamarreño-Ramos, C., Linares-Barranco, A., Serrano-Gotarredona, T., Linares-Barranco, B.,  
*[Multi-Casting Mesh AER: A Scalable Assembly Approach for Reconfigurable Neuromorphic Structured AER Systems. Application to ConvNets](https://doi.org/10.1109/TBCAS.2012.2195725)*,  
IEEE Trans. Biomed. Circuits Syst., 7(1):82-102, 2013.
- <a name="Liu14book"></a>Liu, S.-C., Delbruck, T., Indiveri, G., Whatley, A., Douglas, R.,  
*[Event-Based Neuromorphic Systems](http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118927621.html)*,  
Wiley. ISBN: 978-1-118-92762-5, 2014.
- <a name="Chicca14ieee"></a>Chicca, E., Stefanini, F., Bartolozzi, C., Indiveri, G.,  
*[Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems](http://dx.doi.org/10.1109/JPROC.2014.2313954)*,  
Proc. IEEE, 102(9):1367-1388, 2014.
- <a name="Vanarse16fnins"></a>Vanarse, A., Osseiran, A., Rassau, A,  
*[A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors](http://dx.doi.org/10.3389/fnins.2016.00115)*,  
Front. Neurosci. (2016), 10:115.
- [Liu et al., Signal Process. Mag. 2019](#Liu19msp),  
*Event-Driven Sensing for Efficient Perception: Vision and audition algorithms*.
- [Event Cameras Tutorial - Tobi Delbruck, version 4.1](https://youtu.be/D6rv6q9XyWU), Sep. 18, 2020.
- <a name="Kirkland20spie"></a>Kirkland, P., Di Caterina, G., Soraghan, J., Matich, G.,  
[Neuromorphic technologies for defence and security](https://doi.org/10.1117/12.2575978),  
SPIE vol 11540, Emerging Imaging and Sensing Technologies for Security and Defence V; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III; 2020.


<a name="reviewpapers"></a>
# Review / Overview papers

<a name="reviewpapers-bio"></a>
## Sensor designs, Bio-inspiration
- <a name="Delbruck10iscas"></a>Delbruck, T.,  
*[Activity-driven, event-based vision sensors](https://doi.org/10.1109/ISCAS.2010.5537149)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2010. [PDF](https://e-lab.github.io/data/papers/ISCAS2010actsens.pdf).
- <a name="Posch12jinst"></a>Posch, C.,  
*[Bio-inspired vision](https://doi.org/10.1088/1748-0221/7/01/C01054)*,  
J. of Instrumentation, 7 C01054, 2012.  Bio-inspired explanation of the DVS and the ATIS. [PDF](https://www.neuromorphic-vision.com/public/publications/17/publication.pdf)
- Posch, C., Serrano-Gotarredona, T., Linares-Barranco, B., Delbruck, T.,  
*[Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output](https://doi.org/10.1109/JPROC.2014.2346153),*  
Proc. IEEE (2014), 102(10):1470-1484. [PDF](https://www.neuromorphic-vision.com/public/publications/21/publication.pdf)
- <a name="Posch15bicv"></a>Posch, C.,  
*[Bioinspired vision sensing](https://doi.org/10.1002/9783527680863.ch2)*,  
Biologically Inspired Computer Vision, Wiley-Blackwell, pp. 11-28, 2015. [book index](http://bicv.github.io/toc/index.html)
- <a name="Posch15ieee"></a>Posch, C., Benosman, R., Etienne-Cummings, R.,  
*[How Neuromorphic Image Sensors Steal Tricks From the Human Eye](https://spectrum.ieee.org/biomedical/devices/how-neuromorphic-image-sensors-steal-tricks-from-the-human-eye)*, also published as *[Giving Machines Humanlike Eyes](https://doi.org/10.1109/MSPEC.2015.7335800)*,  
IEEE Spectrum, 52(12):44-49, 2015. [PDF](https://www.neuromorphic-vision.com/public/publications/33/publication.pdf)
- <a name="Cho15sam"></a>Cho, D., Lee, T.-J.,  
*[A Review of Bioinspired Vision Sensors and Their Applications](https://doi.org/10.18494/SAM.2015.1133)*,  
Sensors and Materials, 27(6):447-463, 2015. [PDF](https://myukk.org/SM2017/sm_pdf/SM1083.pdf)
- <a name="Sandamirskaya22scirob"></a>Sandamirskaya, Y., Kaboli, M., Conradt, J., Celikel, T.,  
*[Neuromorphic computing hardware and neural architectures for robotics](https://doi.org/10.1126/scirobotics.abl8419)*,  
Science Robotics, 7(67):eabl8419, 2022.

<a name="reviewpapers-algs"></a>
## Algorithms, Applications
- <a name="Delbruck12eccvw"></a>Delbruck, T.,  
*[Fun with asynchronous vision sensors and processing](https://www.ini.uzh.ch/~tobi/wiki/lib/exe/fetch.php?media=delbruck_funwithasynsensors_2012.pdf)*.  
Computer Vision - ECCV 2012. Workshops and Demonstrations. Springer Berlin/Heidelberg, 2012. A position paper and summary of recent accomplishments of the INI Sensors' group.
- <a name="Delbruck16essderc"></a>Delbruck, T.,  
*[Neuromorophic Vision Sensing and Processing (Invited paper)](https://doi.org/10.1109/ESSDERC.2016.7599576)*,  
46th Eur. Solid-State Device Research Conference (ESSDERC), Lausanne, 2016, pp. 7-14.
- Lakshmi, A., Chakraborty, A., Thakur, C.S.,  
*[Neuromorphic vision: From sensors to event-based algorithms](https://doi.org/10.1002/widm.1310)*,  
Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(4), 2019.
- [Steffen, L. et al., Front. Neurorobot. 2019](#Steffen19fnbot),  
*Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms*.
- [Gallego et al., TPAMI 2020](#Gallego20tpami),  
*[Event-based Vision: A Survey](http://rpg.ifi.uzh.ch/docs/EventVisionSurvey.pdf)*.
- <a name="Chen20msp"></a>Chen, G., Cao, H., Conradt, J., Tang, H., Rohrbein, F., Knoll, A.,  
[Event-Based Neuromorphic Vision for Autonomous Driving: A Paradigm Shift for Bio-Inspired Visual Sensing and Perception](https://doi.org/10.1109/MSP.2020.2985815),  
IEEE Signal Processing Magazine, 37(4):34-49, 2020.
- <a name="Chen20tits"></a>Chen, G., Wang, F., Li, W., Hong, L., Conradt, J., Chen, J., Zhang, Z., Lu, Y., Knoll, A.,  
*[NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations](https://doi.org/10.1109/TITS.2020.3022921)*,  
IEEE Trans. Intelligent Transportation Systems (TITS), 2020.
- <a name="Tayarani21fncir"></a>Tayarani-Najaran, M.-H., Schmuker, M.,  
*[Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review](https://doi.org/10.3389/fncir.2021.610446)*,  
Front. Neural Circuits 15:610446, 2021.
- <a name="Sun21hindawi"></a>Sun, R. Shi, D., Zhang, Y., Li, R., Li, R.,  
*[Data-Driven Technology in Event-Based Vision](https://doi.org/10.1155/2021/6689337)*,  
Complexity, vol. 2021, Article ID 6689337.
- <a name="Bartolozzi22natcomm"></a>Bartolozzi, C., Indiveri, G., Donati, E.,  
*[Embodied neuromorphic intelligence](https://doi.org/10.1038/s41467-022-28487-2)*,  
Nat. Commun. 13:1024, 2022.
- <a name="Zou22mir"></a>Zou, XL., Huang, T.J., Wu, S.,  
*[Towards a New Paradigm for Brain-inspired Computer Vision](https://doi.org/10.1007/s11633-022-1370-z)*,  
Mach. Intell. Res., 19:412-424, 2022.
- <a name="Gehrig22arxiv"></a>Gehrig, D., Scaramuzza, D.,  
*[Are High-Resolution Cameras Really Needed?](https://arxiv.org/abs/2203.14672)*,  
arXiv, 2022. [YouTube](https://youtu.be/HV9_FhS-f88), [code](https://uzh-rpg.github.io/eres/).  
- <a name="Ercan23cvprw"></a>Ercan, B., Eker, O., Erdem, A., Erdem, E.,  
*[EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Ercan_EVREAL_Towards_a_Comprehensive_Benchmark_and_Analysis_Suite_for_Event-Based_CVPRW_2023_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2023. [PDF](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Ercan_EVREAL_Towards_a_Comprehensive_Benchmark_and_Analysis_Suite_for_Event-Based_CVPRW_2023_paper.pdf), [Project Page](https://ercanburak.github.io/evreal.html), [Suppl.](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/supplemental/Ercan_EVREAL_Towards_a_CVPRW_2023_supplemental.zip), [Code](https://github.com/ercanburak/EVREAL).
- <a name="Tapia23iros"></a>Tapia, R., Rodríguez-Gómez, J.P., Sanchez-Diaz, J.A., Gañán, F.J., Rodríguez, I.G., Luna-Santamaria, J., Martínez-De Dios, J.R., Ollero, A.,  
*[A Comparison Between Framed-Based and Event-Based Cameras for Flapping-Wing Robot Perception](https://doi.org/10.1109/IROS55552.2023.10342500)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2023, pp. 3025-3032. [PDF](https://arxiv.org/pdf/2309.05450), [YouTube](https://www.youtube.com/watch?v=a0X2NGPtY8w).  
- <a name="Ghosh24stereo"></a>Ghosh, S., Gallego, G.,  
*[Event-based Stereo Depth Estimation: A Survey](https://arxiv.org/abs/2409.17680)*,  
arXiv 2024.

<a name="algorithms"></a>
# Algorithms

<a name="feature-detection"></a>
## Feature Detection and Tracking
- <a name="Litzenberger06dspws"></a>Litzenberger, M., Posch, C., Bauer, D., Belbachir, A. N., Schon. P., Kohn, B., Garn, H.,  
*[Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor](https://doi.org/10.1109/DSPWS.2006.265448)*,  
IEEE 12th Digital Signal Proc. Workshop and 4th IEEE Signal Proc. Education Workshop, Teton National Park, WY, 2006, pp. 173-178. [PDF](http://www.belbachir.info/PDF/dsp2006.pdf)
    - <a name="Litzenberger06itsc"></a>Litzenberger, M., Kohn, B., Belbachir, A.N., Donath, N., Gritsch, G., Garn, H., Posch, C., Schraml, S.,  
*[Estimation of Vehicle Speed Based on Asynchronous Data from a Silicon Retina Optical Sensor](https://doi.org/10.1109/ITSC.2006.1706816)*,  
IEEE Intelligent Transportation Systems Conf. (ITSC), 2006, pp. 653-658. [PDF](http://belbachir.info/PDF/itsc2006.pdf)
    - <a name="Bauer07ejes"></a>Bauer, D., Belbachir, A. N., Donath, N., Gritsch, G., Kohn, B., Litzenberger, M., Posch, C., Schön, P., Schraml, S.,  
*[Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor](https://link.springer.com/article/10.1155/2007/82174)*,  
EURASIP J. Embedded Systems, 2007:082174. [PDF](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.385.424&rep=rep1&type=pdf)
    - <a name="Litzenberger07icdsc"></a>Litzenberger, M., Belbachir, N., Schon, P., Posch, C.,  
*[Embedded Smart Camera for High Speed Vision](https://doi.org/10.1109/ICDSC.2007.4357509)*,  
ACM/IEEE Int. Conf. on Distributed Smart Cameras, 2007. [PDF](http://belbachir.info/PDF/icdsc2007.pdf)
- <a name="Ni12tro"></a>Ni, Z., Bolopion, A., Agnus, J., Benosman, R., Regnier, S.,  
*[Asynchronous event-based visual shape tracking for stable haptic feedback in microrobotics](https://doi.org/10.1109/TRO.2012.2198930)*,  
IEEE Trans. Robot. (TRO), 28(5):1081-1089, 2012. [PDF](https://www.neuromorphic-vision.com/public/publications/15/publication.pdf)
    - [Ni, Ph.D. Thesis, 2013](#Ni13PhD),  
*Asynchronous Event Based Vision:  Algorithms and Applications to Microrobotics*.
    - <a name="Ni15neco"></a>Ni, Z., Ieng, S. H., Posch, C., Regnier, S., Benosman, R.,  
*[Visual Tracking Using Neuromorphic Asynchronous Event-Based Cameras](https://doi.org/10.1162/NECO_a_00720)*,  
Neural Computation (2015), 27(4):925-953. [PDF](https://www.neuromorphic-vision.com/public/publications/40/publication.pdf), [YouTube](https://youtu.be/eQ7reEN9PrA)
- <a name="Piatkowska12cvprw"></a>Piatkowska, E., Belbachir, A. N., Schraml, S., Gelautz, M.,  
*[Spatiotemporal multiple persons tracking using Dynamic Vision Sensor](https://doi.org/10.1109/CVPRW.2012.6238892)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012, pp. 35-40. [PDF](https://publik.tuwien.ac.at/files/PubDat_209369.pdf)
- <a name="Lagorce15fnins"></a>Lagorce, X., Ieng, S.-H., Clady, X., Pfeiffer, M., Benosman, R.,  
*[Spatiotemporal features for asynchronous event-based data](http://dx.doi.org/10.3389/fnins.2015.00046)*,  
Front. Neurosci. (2015), 9:46.
    - <a name="Lagorce13iros"></a>Lagorce, X., Ieng, S. H., Benosman, R.,  
*[Event-based features for robotic vision](http://dx.doi.org/10.1109/IROS.2013.6696960)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2013, pp. 4214-4219.
- <a name="Saner14vmv"></a>Saner, D., Wang, O., Heinzle, S., Pritch, Y., Smolic, A., Sorkine-Hornung, A., Gross, M.,  
*[High-Speed Object Tracking Using an Asynchronous Temporal Contrast Sensor](http://dx.doi.org/10.2312/vmv.20141280)*,  
Int. Symp. Vision, Modeling and Visualization (VMV), 2014. [PDF](http://ahornung.net/files/pub/2014-vmv-siliconretina-saner.pdf)
- <a name="Lagorce15tnnls"></a>Lagorce, X., Meyer, C., Ieng, S. H., Filliat, D., Benosman, R.,  
*[Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking](https://doi.org/10.1109/TNNLS.2014.2352401)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 26(8):1710-1720, 2015. [PDF](https://www.neuromorphic-vision.com/public/publications/28/publication.pdf), [YouTube](https://youtu.be/ze8Wgou9yA4)
    - <a name="Lagorce14biocas"></a>Lagorce, X., Meyer, C., Ieng, S. H., Filliat, D., Benosman, R.,  
*[Live demonstration: Neuromorphic event-based multi-kernel algorithm for high speed visual features tracking](https://doi.org/10.1109/BioCAS.2014.6981681)*,  
IEEE Biomedical Circuits and Systems Conference (BioCAS), 2014, pp. 178.
- <a name="Reverter15tnnls"></a>Reverter Valeiras, D., Lagorce, X., Clady, X., Bartolozzi, C., Ieng, S., Benosman, R.,  
*[An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking](https://doi.org/10.1109/TNNLS.2015.2401834)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 26(12):3045-3059, 2015. [PDF](https://www.neuromorphic-vision.com/public/publications/23/publication.pdf), [YouTube](https://youtu.be/XeQYNYESJtQ)
- <a name="LinaresBarranco15iscas"></a>Linares-Barranco, A., Gómez-Rodríguez, F., Villanueva, V., Longinotti, L., Delbrück, T.,    
*[A USB3.0 FPGA event-based filtering and tracking framework for dynamic vision sensors](https://doi.org/10.1109/ISCAS.2015.7169172)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
- <a name="LinaresBarranco15iscas"></a>Leow, H. S., Nikolic, K.,  
*[Machine vision using combined frame-based and event-based vision sensor](https://doi.org/10.1109/ISCAS.2015.7168731)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
- <a name="Liu16iscas"></a>Liu, H., Moeys, D. P., Das, G., Neil, D., Liu, S.-C., Delbruck, T.,  
*[Combined frame- and event-based detection and tracking](https://doi.org/10.1109/ISCAS.2016.7539103)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2016.
- <a name="Tedaldi16ebccsp"></a>Tedaldi, D., Gallego, G., Mueggler, E., Scaramuzza, D.,  
*[Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS)](https://doi.org/10.1109/EBCCSP.2016.7605086)*,  
IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2016. [PDF](http://rpg.ifi.uzh.ch/docs/EBCCSP16_Tedaldi.pdf), [YouTube](https://www.youtube.com/watch?v=nglfEkiK308)
    - [Kueng et al., IROS 2016](#Kueng16iros)
*Low-Latency Visual Odometry using Event-based Feature Tracks*.
- <a name="Brandli16ebccsp"></a>Braendli, C., Strubel, J., Keller, S., Scaramuzza, D., Delbruck, T.,  
*[ELiSeD - An Event-Based Line Segment Detector](https://doi.org/10.1109/EBCCSP.2016.7605244)*,  
Int. Conf. on Event-Based Control Comm. and Signal Proc. (EBCCSP), 2016. [PDF](http://rpg.ifi.uzh.ch/docs/EBCCSP16_Braendli.pdf)
- <a name="Glover16iros"></a>Glover, A. and Bartolozzi, C.,  
*[Event-driven ball detection and gaze fixation in clutter](https://doi.org/10.1109/IROS.2016.7759345)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2016, pp. 2203-2208. [YouTube](https://youtu.be/n6qTkw5U7YI), [Code](https://github.com/robotology/event-driven)
    - <a name="Glover17iros"></a>Glover, A. and Bartolozzi, C.,  
*[Robust Visual Tracking with a Freely-moving Event Camera](https://doi.org/10.1109/IROS.2017.8206226)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2017. [YouTube](https://youtu.be/xS-7xYRYSLc), [Code](https://github.com/robotology/event-driven)
    - <a name="Glover18irosw"></a>Glover, A., Stokes, A.B., Furber, S., Bartolozzi, C.,  
*[ATIS + SpiNNaker: a Fully Event-based Visual Tracking Demonstration](https://arxiv.org/pdf/1912.01320)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems Workshops (IROSW), 2018. Workshop on Unconventional Sensing and Processing for Robotic Visual Perception.
- <a name="Clady17fnins"></a>Clady, X., Maro, J.-M., Barré, S., Benosman, R. B.,  
*[A Motion-Based Feature for Event-Based Pattern Recognition](https://doi.org/10.3389/fnins.2016.00594)*.  
Front. Neurosci. (2017), 10:594. [PDF](https://www.neuromorphic-vision.com/public/publications/49/publication.pdf)
- <a name="Zhu17icra"></a>Zhu, A., Atanasov, N., Daniilidis, K.,  
*[Event-based Feature Tracking with Probabilistic Data Association](https://doi.org/10.1109/ICRA.2017.7989517)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2017. [PDF](https://fling.seas.upenn.edu/~alexzhu/dynamic/wp-content/uploads/2017/07/EventBasedFeatureTrackingICRA2017.pdf), [YouTube](https://youtu.be/m93XCqAS6Fc), [Code](https://github.com/daniilidis-group/event_feature_tracking)
- <a name="BarriosAviles18electronics"></a>Barrios-Avilés, J., Iakymchuk, T., Samaniego, J., Medus, L.D., Rosado-Muñoz, A.,  
*[Movement Detection with Event-Based Cameras: Comparison with Frame-Based Cameras in Robot Object Tracking Using Powerlink Communication](https://doi.org/10.3390/electronics7110304)*,  
Electronics 2018, 7, 304. [PDF pre-print](https://arxiv.org/abs/1707.07188)
- <a name="Li17bmvc"></a>Li, J., Shi, F., Liu, W., Zou, D., Wang, Q., Park, P.K.J., Ryu, H.,  
*[Adaptive Temporal Pooling for Object Detection using Dynamic Vision Sensor](https://www.dropbox.com/s/m77i7cqqy7xbg51/0099.pdf?dl=1)*,  
British Machine Vision Conf. (BMVC), 2017.
- <a name="Peng17tnnls"></a>Peng, X., Zhao, B., Yan, R., Tang H., Yi, Z.,  
*[Bag of Events: An Efficient Probability-Based Feature Extraction Method for AER Image Sensors](http://dx.doi.org/10.1109/TNNLS.2016.2536741)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 28(4):791-803, 2017.
- <a name="Ramesh19tpami"></a>Ramesh, B., Yang, H., Orchard, G., Le Thi, N.A., Xiang, C,  
*[DART: Distribution Aware Retinal Transform for Event-based Cameras](https://doi.org/10.1109/TPAMI.2019.2919301)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2019. [PDF](https://arxiv.org/pdf/1710.10800.pdf)
- <a name="Gehrig19ijcv"></a>Gehrig, D., Rebecq, H., Gallego, G., Scaramuzza, D.,  
*[EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames](http://rpg.ifi.uzh.ch/docs/IJCV19_Gehrig.pdf)*,  
Int. J. Computer Vision (IJCV), 2019. [YouTube](https://youtu.be/ZyD1YPW1h4U), [Tracking code](https://github.com/uzh-rpg/rpg_eklt), [Evaluation code](https://github.com/uzh-rpg/rpg_feature_tracking_analysis)
    - <a name="Gehrig18eccv"></a>Gehrig, D., Rebecq, H., Gallego, G., Scaramuzza, D.,  
*[Asynchronous, Photometric Feature Tracking using Events and Frames](http://rpg.ifi.uzh.ch/docs/ECCV18_Gehrig.pdf)*,  
European Conf. Computer Vision (ECCV), 2018. [Poster](http://rpg.ifi.uzh.ch/docs/ECCV18_Gehrig_poster.pdf), [YouTube](https://youtu.be/A7UfeUnG6c4), [Oral presentation](https://youtu.be/7EvY8SxdLl8), [Tracking code](https://github.com/uzh-rpg/rpg_eklt), [Evaluation code](https://github.com/uzh-rpg/rpg_feature_tracking_analysis)
- <a name="Everding18fnbot"></a>Everding, L., Conradt, J.,  
*[Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors](https://doi.org/10.3389/fnbot.2018.00004)*,  
Front. Neurorobot. 12:4, 2018.  [Videos](http://www.frontiersin.org/articles/10.3389/fnbot.2018.00004/full#supplementary-material)
- <a name="LinaresBarrancoA18entropy"></a>Linares-Barranco, A., Liu, H., Rios-Navarro, A., Gomez-Rodriguez, F., Moeys, D., Delbruck, T.  
*[Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics](https://doi.org/10.3390/e20060475)*,  
Entropy 2018, 20(6), 475.  
- <a name="Mitrokhin18iros"></a>Mitrokhin, A., Fermüller, C., Parameshwara, C., Aloimonos, Y.,  
*[Event-based Moving Object Detection and Tracking](https://doi.org/10.1109/IROS.2018.8593805)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018. [PDF](https://arxiv.org/pdf/1803.04523.pdf), [YouTube](https://youtu.be/UCAJi0ZFaZ8), [Project page and Dataset](http://prg.cs.umd.edu/BetterFlow.html)
- <a name="Iacono18iros"></a>Iacono, M., Weber, S., Glover, A., Bartolozzi, C.,  
*[Towards Event-Driven Object Detection with Off-The-Shelf Deep Learning](https://doi.org/10.1109/IROS.2018.8594119)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018.
- <a name="Ramesh18bmvc"></a>Ramesh, B., Zhang, S., Lee, Z.-W., Gao, Z., Orchard, G., Xiang, C.,  
*[Long-term object tracking with a moving event camera](http://bmvc2018.org/contents/papers/0814.pdf)*,  
British Machine Vision Conf. (BMVC), 2018.  [Video](http://bmvc2018.org/contents/supplementary/video/0814_video.mp4)
    - <a name="Ramesh20arxiv"></a>Ramesh, B., Zhang, S., Yang, H., Ussa, A., Ong, M., Orchard, G., Xiang, C.,  
*[e-TLD: Event-based Framework for Dynamic Object Tracking](https://arxiv.org/pdf/2009.00855.pdf)*,  
arXiv, 2020.
- <a name="Dardelet18arxiv"></a>Dardelet, L., Ieng, S.-H., Benosman, R.,  
*[Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours](https://arxiv.org/pdf/1811.07839)*,  
arXiv:1811.07839, 2018.
- <a name="Wu18chreoc"></a>Wu, J., Zhang, K., Zhang, Y., Xie, X., Shi, G.,  
*[High-Speed Object Tracking with Dynamic Vision Sensor](https://doi.org/10.1007/978-981-13-6553-9_18)*,  
China High Resolution Earth Observation Conference (CHREOC), 2018.
- <a name="Huang18tcsvt"></a>Huang, J., Wang, S., Guo, M., Chen, S.,  
*[Event-Guided Structured Output Tracking of Fast-Moving Objects Using a CeleX Sensor](https://doi.org/10.1109/TCSVT.2018.2841516)*,  
IEEE Trans. Circuits Syst. Video Technol. (TCSVT), 28(9):2413-2417, 2018.
- <a name="Renner19cvprw"></a>Renner, A., Evanusa, M., Sandamirskaya, Y.,  
*[Event-based attention and tracking on neuromorphic hardware](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Renner_Event-Based_Attention_and_Tracking_on_Neuromorphic_Hardware_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [Video pitch](https://youtu.be/eWBEJOr056E)
- <a name="Foster19elim"></a>Foster, B.J., Ye, D.H., Bouman, C.A.,  
*[Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter](https://www.ingentaconnect.com/contentone/ist/ei/2019/00002019/00000013/art00002?crawler=true&mimetype=application/pdf)*,  
IS&T International Symposium on Electronic Imaging 2019. Computational Imaging XVII.
- <a name="Alzugaray193dv"></a>Alzugaray, I., Chli, M.,  
*[Asynchronous Multi-Hypothesis Tracking of Features with Event Cameras](https://doi.org/10.3929/ethz-b-000360434)*,  
IEEE Int. Conf. 3D Vision (3DV), 2019. [PDF](https://doi.org/10.3929/ethz-b-000360434), [Code](https://github.com/ialzugaray/haste), [YouTube](https://youtu.be/eguV_AIbteU)
- <a name="LinaresBarranco19access"></a>Linares-Barranco, A., Perez-Pena, F., Moeys, D.P., Gomez-Rodriguez, F., Jimenez-Moreno, G., Delbruck, T.  
*[Low Latency Event-based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications](https://doi.org/10.1109/ACCESS.2019.2941282)*,  
IEEE Access, 7:134926-134942, 2019. [Code](https://github.com/RTC-research-group/EDIP_library)
- <a name="Li19access"></a>Li, K., Shi, D., Zhang, Y., Li, R., Qin, W., Li, R.,  
*[Feature Tracking Based on Line Segments With the Dynamic and Active-Pixel Vision Sensor (DAVIS)](https://doi.org/10.1109/ACCESS.2019.2933594)*,  
IEEE Access, 7:110874-110883, 2019.
- <a name="Bolten19iccs"></a>Bolten T., Pohle-Fröhlich R., Tönnies K.D.,  
*[Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor](https://doi.org/10.1007/978-3-030-22750-0_13)*,  
Int. Conf. Computational Science (ICCS) 2019. [PDF](https://www.hs-niederrhein.de/fileadmin/dateien/Institute_und_Kompetenzzentren/iPattern/selfarchived/bolten-iccs-2019.pdf)
- <a name="Chen19mm"></a>Chen, H., Wu, Q., Liang, Y., Gao, X., Wang, H.,  
*[Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://doi.org/10.1145/3343031.3350975)*,  
ACM Int. Conf. on Multimedia (MM), 2019.
- <a name="Reverter19tnnls"></a>Reverter Valeiras, D., Clady, X., Ieng, S.-H., Benosman, R.,  
*[Event-Based Line Fitting and Segment Detection Using a Neuromorphic Visual Sensor](https://doi.org/10.1109/TNNLS.2018.2807983)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 30(4):1218-1230, 2019. [PDF](https://www.neuromorphic-vision.com/public/publications/51/publication.pdf)
- <a name="Li19fnbot"></a>Li, H., Shi, L., 
*[Robust Event-Based Object Tracking Combining Correlation Filter and CNN Representation](https://doi.org/10.3389/fnbot.2019.00082)*,  
Front. Neurorobot. 13:82, 2019. [Dataset](https://figshare.com/s/70565903453eef7c3965)
- <a name="Chen2020arxiv"></a>Chen, H., Suter, D., Wu, Q., Wang, H.,  
*[End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking](https://doi.org/10.1609/aaai.v34i07.6625)*,  
AAAI Conf. Artificial Intelligence, 2020. [PDF](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-ChenH.2586.pdf), [PDF](https://arxiv.org/pdf/2002.05911).
- <a name="Monforte20aicas"></a>Monforte, M., Arriandiaga, A., Glover, A., Bartolozzi, C.,  
*[Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories](https://arxiv.org/pdf/2001.01248)*,  
IEEE Int. Conf. Artificial Intelligence Circuits and Systems (AICAS), 2020.
- <a name="Sengupta20aicas"></a>Sengupta, J. P., Kubendran, R., Neftci, E., Andreou, A. G.,  
*[High-Speed, Real-Time, Spike-Based Object Tracking and Path Prediction on Google Edge TPU.](https://par.nsf.gov/servlets/purl/10212648)*  
IEEE Int. Conf. Artificial Intelligence Circuits and Systems (AICAS), 2020, pp. 134-135.  
- <a name="Seok20wacv"></a>Seok, H., Lim, J.,  
*[Robust Feature Tracking in DVS Event Stream using Bezier Mapping](http://openaccess.thecvf.com/content_WACV_2020/papers/Seok_Robust_Feature_Tracking_in_DVS_Event_Stream_using_Bezier_Mapping_WACV_2020_paper.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2020.  [YouTube](https://youtu.be/mskBdueW9Hc)
- <a name="Xu20cvpr"></a>Xu, L., Xu, W., Golyanik, V., Habermann, M., Fang, L., Theobalt, C.,  
*[EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera](https://arxiv.org/pdf/1908.11505)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [ZDNet news](https://www.zdnet.com/article/high-speed-motion-capture-using-a-single-event-camera/)
- <a name="RodriguezGomez20icra"></a>Rodríguez-Gómez, J.P., Gómez Eguíluz, A., Martínez-de Dios, J.R., Ollero, A.,  
*[Asynchronous event-based clustering and tracking for intrusion monitoring](https://ras.papercept.net/proceedings/ICRA20/1523.pdf)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2020. [PDF](https://zenodo.org/record/3816654#.XxHQ9hFS9hE).
- [Boettiger, J. P., MSc 2020](#Boettiger20MSc),
*A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors*.
- <a name="Sarmadi20arxiv"></a>Sarmadi, H., Muñoz-Salinas, R., Olivares-Mendez, M. A., Medina-Carnicer, R.,  
*[Detection of Binary Square Fiducial Markers Using an Event Camera](https://arxiv.org/pdf/2012.06516)*,  
arXiv, 2020.
- <a name="Alzugaray20bmvc"></a>Alzugaray, I., Chli, M.,  
*[HASTE: multi-Hypothesis Asynchronous Speeded-up Tracking of Events](https://www.bmvc2020-conference.com/conference/papers/paper_0744.html)*,  
British Machine Vision Conf. (BMVC), 2020. [PDF](https://www.bmvc2020-conference.com/assets/papers/0744.pdf), [Suppl. Mat.](https://www.bmvc2020-conference.com/assets/supp/0744_supp.zip), [Code](https://github.com/ialzugaray/haste), [Presentation](https://www.bmvc2020-conference.com/conference/papers/paper_0744.html), [Youtube](https://youtu.be/6DZxIzrVLcI)
- <a name="Liu21arxiv"></a>Liu, Z., Fu, Y.,  
*[e-ACJ: Accurate Junction Extraction For Event Cameras](https://arxiv.org/pdf/2101.11251)*,  
arXiv, 2021.
- <a name="Dong21icmva"></a>Dong, Y., Zhang, T.,  
*[Standard and Event Cameras Fusion for Feature Tracking](https://dl.acm.org/doi/10.1145/3459066.3459075)*,  
Int. Conf. on Machine Vision and Applications (ICMVA), 2021. [Code](https://github.com/LarryDong/FusionTracking)
- <a name="Mondal21iccvw"></a>Mondal, A., Shashant, R., Giraldo, J. H., Bouwmans, T., Chowdhury, A. S.,  
*[Moving Object Detection for Event-based Vision using Graph Spectral Clustering](https://openaccess.thecvf.com/content/ICCV2021W/GSP-CV/papers/Mondal_Moving_Object_Detection_for_Event-Based_Vision_Using_Graph_Spectral_Clustering_ICCVW_2021_paper.pdf)*,  
IEEE Int. Conf. Computer Vision Workshop (ICCVW), 2021. [Youtube](https://youtu.be/ST6Z-3SlNS4), [Code](https://github.com/anindya2001/GSCEventMOD).
- <a name="Wang21arxiv"></a>Xiao Wang, Jianing Li, Lin Zhu, Zhipeng Zhang, Zhe Chen, Xin Li, Yaowei Wang, Yonghong Tian, Feng Wu,  
*[VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://arxiv.org/abs/2108.05015)*,  
arXiv, 2021. [Code](https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark)
- [Alzugaray, I., Ph.D. Thesis, 2022](#Alzugaray22PhD),  
*Event-driven Feature Detection and Tracking for Visual SLAM*.
- <a name="zhang2021multi"></a>Zhang, J., Zhao, K., Dong, B., Fu, Y., Wang, Y., Yang, X., Yin, B.,  
*[Multi-domain collaborative feature representation for robust visual object tracking](https://doi.org/10.1007/s00371-021-02237-9)*,  
The Visual Computer, 2021. [PDF](https://link.springer.com/article/10.1007/s00371-021-02237-9), [Project](https://zhangjiqing.com/publication/multi-domain-collaborative-feature-representation-for-robust-visual-object-tracking-the-visual-computer-2021-proc-cgi-2021-/).
- <a name="Zhang2021iccv"></a>Zhang, J., Yang, X., Fu, Y., Wei, X., Yin, B., Dong, B.,  
*[Object Tracking by Jointly Exploiting Frame and Event Domain](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Object_Tracking_by_Jointly_Exploiting_Frame_and_Event_Domain_ICCV_2021_paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [Project](https://zhangjiqing.com/publication/iccv21_fe108_tracking/), [PDF](https://arxiv.org/abs/2109.09052), [code](https://github.com/Jee-King/ICCV2021_Event_Frame_Tracking), [dataset](https://zhangjiqing.com/dataset/).
- <a name="Dietsche21iros"></a>Dietsche, A., Cioffi, G., Hidalgo-Carrio, J., Scaramuzza, D.,  
*[Powerline Tracking with Event Cameras](http://dx.doi.org/10.1109/IROS51168.2021.9636824)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021. [PDF](https://rpg.ifi.uzh.ch/docs/IROS21_Dietsche.pdf), [dataset](https://download.ifi.uzh.ch/rpg/powerline_tracking_dataset/), [YouTube](https://www.youtube.com/watch?v=KnBJqed5qDI), [code](https://github.com/uzh-rpg/line_tracking_with_event_cameras).
- <a name="Li21icra"></a>Li, H., Stueckler, J.,  
*[Tracking 6-DoF Object Motion from Events and Frames](https://arxiv.org/pdf/2103.15568.pdf)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2021.
- <a name="Zhang2022cvpr"></a>Zhang, J., Dong, B., Zhang, H., Ding, J., Heide, F., Yin, B., Yang, X.,  
*[Spiking Transformers for Event-based Single Object Tracking](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Spiking_Transformers_for_Event-Based_Single_Object_Tracking_CVPR_2022_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [Project](https://zhangjiqing.com/publication/stnet/), [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Spiking_Transformers_for_Event-Based_Single_Object_Tracking_CVPR_2022_paper.pdf), [code](https://github.com/Jee-King/CVPR2022_STNet).
- [Gao, et at., FPGA, 2022](#Gao22FPGA),  
*REMOT: A Hardware-Software Architecture for Attention-Guided Multi-Object Tracking with Dynamic Vision Sensors on FPGAs*.
- <a name="el2022high"></a>El Shair, Z., Rawashdeh, S.A.,  
*[High-Temporal-Resolution Object Detection and Tracking using Images and Events](https://doi.org/10.3390/jimaging8080210)*,  
Journal of Imaging, 2022. [PDF](https://www.mdpi.com/2313-433X/8/8/210/pdf), [dataset](http://sar-lab.net/event-based-vehicle-detection-and-tracking-dataset/).
- <a name="hu2022eCDT"></a> Hu, S., Kim, Y., Lim, H., Lee, A., Myung, H.,  
[eCDT: Event Clustering for Simultaneous Feature Detection and Tracking](https://doi.org/10.1109/IROS47612.2022.9981451),  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2022. [YouTube](https://www.youtube.com/watch?v=eS-3PP-8sek&ab_channel=KAISTUrbanRoboticsLab)
- <a name="zhu2022learning"></a>Zhu, Z., Hou, J., Lyu, X.,  
*[Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds](https://openreview.net/pdf?id=hTxYJAKY85)*,  
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. [PDF](https://openreview.net/pdf?id=hTxYJAKY85), [code](https://github.com/ZHU-Zhiyu/Event-tracking).
- <a name="Elshair22opteng"></a>El Shair, Z., Rawashdeh, S.A.,  
*[High-temporal-resolution event-based vehicle detection and tracking](https://doi.org/10.1117/1.OE.62.3.031209)*,  
Optical Engineering, 2022. [dataset](http://sar-lab.net/event-based-vehicle-detection-and-tracking-dataset/).
- <a name="Messikommer23cvpr"></a>Messikommer, N., Fang, C., Gehrig, M., Scaramuzza, D.,  
*[Data-driven Feature Tracking for Event Cameras](https://arxiv.org/abs/2211.12826)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR23_Messikommer.pdf), [YouTube](https://youtu.be/dtkXvNXcWRY), [code](https://github.com/uzh-rpg/deep_ev_tracker).  
- <a name="Pedersen23nice"></a>Pedersen, J., Singhal, R., Conradt, J.,<br/>
  [Translation and Scale Invariance for Event-Based Object tracking](https://dl.acm.org/doi/10.1145/3584954.3584996),<br/>
  Proc. Annual Neuro-Inspired Computational Elements Conf. (NICE), 2023, pp. 86-91. <a href="https://jepedersen.dk/202304_nice_object">Website</a>, <a href="https://github.com/jegp/coordinate-regression">code</a>, <a href="https://jepedersen.dk/slides/2304_NICE/2304_object_tracking.html">presentation</a>.
- <a name="Zhu23iccv"></a>Zhu, Z., Hou, J., Wu DO.,<br/>
*[Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers](https://arxiv.org/abs/2307.04129).*,<br/>
IEEE Int. Conf. Computer Vision (ICCV), 2023., [code](https://github.com/ZHU-Zhiyu/High-Rank_RGB-Event_Tracker).
- <a name="Wang24tro"></a>Wang, Z., Molloy, T., van Goor, P., Mahony, R.,<br/>
*[Asynchronous Blob Tracker for Event Cameras](https://doi.org/10.1109/TRO.2024.3454410).*,<br/>
IEEE Trans. Robot. (TRO), 2024. [PDF](https://arxiv.org/pdf/2307.10593.pdf), [Video](https://www.youtube.com/watch?v=L_wJjhcToOU), [Project page](https://github.com/ziweiWWANG/AEB-Tracker).
- <a name="Nagaraj23icra"></a>Nagaraj, M., Liyanagedera, C.M., Roy, K.,<br/>
*[DOTIE - Detecting Objects through Temporal Isolation of Events using a Spiking Architecture](https://ieeexplore.ieee.org/abstract/document/10161164).*,<br/>
IEEE Int. Conf. Robotics and Automation (ICRA), 2023. [Arxiv](https://arxiv.org/abs/2210.00975), [CVPR 2023 workshop](https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_Live_Demonstration_Real-time_Event-based_Speed_Detection_using_Spiking_Neural_Networks.pdf), [Code](https://github.com/manishnagaraj/DOTIE).
- [Gao et al., ICCV 2023](#Gao23iccv), *A 5-Point Minimal Solver for Event Camera Relative Motion Estimation*.
    - [Gao et al., CVPR 2024](#Gao24cvpr), *An N-Point Linear Solver for Line and Motion Estimation with Event Cameras*.
- <a name="Li24cvpr"></a>Li, S., Zhou, Z., Xue, Z., Li, Y., Du, S., Gao, Y.,<br/>
  *3D Feature Tracking via Event Camera*,<br/>
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2024. [Code](https://github.com/lisiqi19971013/E-3DTrack), [Dataset](https://github.com/lisiqi19971013/event-based-datasets).
- <a name="Kang24icra"></a>Kang, Y., Caron, G., Ishikawa, R., Escande, A., Chappellet, K., Sagawa, R., Oishi, T.,<br/>
*[Direct 3D model-based object tracking with event camera by motion interpolation](https://www.cvl.iis.u-tokyo.ac.jp/~kyf/ICRA2024/BIAM.pdf).*,<br/>
IEEE Int. Conf. Robotics and Automation (ICRA), 2024. [Dataset](https://www.cvl.iis.u-tokyo.ac.jp/~kyf/ICRA2024/).

<a name="corner-detection"></a>
### Corner Detection and Tracking
- <a name="Clady15neunet"></a>Clady, X., Ieng, S.-H., Benosman, R.,  
*[Asynchronous event-based corner detection and matching](https://doi.org/10.1016/j.neunet.2015.02.013)*,  
Neural Networks (2015), 66:91-106. [PDF](https://www.neuromorphic-vision.com/public/publications/38/publication.pdf)
- <a name="Vasco16iros"></a>Vasco, V., Glover, A., Bartolozzi, C.,  
*[Fast event-based Harris corner detection exploiting the advantages of event-driven cameras](https://doi.org/10.1109/IROS.2016.7759610)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2016, pp. 4144-4149. [YouTube](https://youtu.be/YkI7AfBDBKE), [Code](https://github.com/robotology/event-driven)
- <a name="Mueggler17bmvc"></a>Mueggler, E., Bartolozzi, C., Scaramuzza, D.,  
*[Fast Event-based Corner Detection](http://rpg.ifi.uzh.ch/docs/BMVC17_Mueggler.pdf)*,  
British Machine Vision Conf. (BMVC), 2017. [YouTube](https://youtu.be/tgvM4ELesgI), [Code](https://github.com/uzh-rpg/rpg_corner_events)
    - <a name="Liu19cvprw"></a>Liu, H., Kao, W.-T., Delbruck, T.,  
*[Live Demonstration: A Real-time Event-based Fast Corner Detection Demo based on FPGA](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Liu_Live_Demonstration_A_Real-Time_Event-Based_Fast_Corner_Detection_Demo_Based_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
    - Standalone Rust implementation, [Code](https://github.com/ac-freeman/dvs-fast-corners)
- <a name="Alzugaray18ral"></a>Alzugaray, I., Chli, M.,  
*[Asynchronous Corner Detection and Tracking for Event Cameras in Real Time](http://dx.doi.org/10.1109/LRA.2018.2849882)*,  
IEEE Robotics and Automation Letters (RA-L), 3(4):3177-3184, Oct. 2018.  [PDF](https://doi.org/10.3929/ethz-b-000277131), [YouTube](https://youtu.be/bKUAZ7IQcf0), [Code](https://github.com/ialzugaray/arc_star_ros).
- <a name="Alzugaray183dv"></a>Alzugaray, I., Chli, M.,  
*[ACE: An Efficient Asynchronous Corner Tracker for Event Cameras](https://doi.org/10.1109/3DV.2018.00080)*,  
IEEE Int. Conf. 3D Vision (3DV), 2018. [PDF](https://doi.org/10.3929/ethz-b-000291763), [YouTube](https://youtu.be/I31yQqmCsfs)
- <a name="Scheerlinck19ral"></a>Scheerlinck, C., Barnes, N., Mahony, R.,  
*[Asynchronous Spatial Image Convolutions for Event Cameras](https://doi.org/10.1109/LRA.2019.2893427)*,  
IEEE Robotics and Automation Letters (RA-L), 4(2):816-822, Apr. 2019.  [PDF](https://cedric-scheerlinck.github.io/files/2018_event_convolutions.pdf), [Website](https://cedric-scheerlinck.github.io/2018_event_convolutions)
- <a name="Manderscheid19cvpr"></a>Manderscheid, J., Sironi, A., Bourdis, N., Migliore, D., Lepetit, V.,  
*[Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](http://openaccess.thecvf.com/content_CVPR_2019/html/Manderscheid_Speed_Invariant_Time_Surface_for_Learning_to_Detect_Corner_Points_CVPR_2019_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019.  [PDF](https://arxiv.org/pdf/1903.11332)
- <a name="Manderscheid19cvpr"></a>Li, R., Shi, D., Zhang, Y., Li, K., Li, R.,  
*[FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://doi.org/10.1109/IROS40897.2019.8968491)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2019. [PDF](https://arxiv.org/pdf/1906.10925)
- <a name="Mohamed20icpr"></a>Mohamed, S. A. S., Yasin, J. N., Haghbayan, M.-H., Miele, A., Heikkonen, J., Tenhunen, H., Plosila, J.,  
*[Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors](https://arxiv.org/pdf/2010.15507)*,  
Int. Conf. Pattern Recognition (ICPR), 2020.
- <a name="Mohamed20isvc"></a>Mohamed, S. A. S., Yasin, J. N., Haghbayan, M.-H., Miele, A., Heikkonen, J., Tenhunen, H., Plosila, J.,  
*[Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras](https://arxiv.org/pdf/2010.15510)*,  
Int. Symposium on Visual Computing (ISVC), 2020.
- <a name="Yilmaz21ji"></a>Yılmaz, Ö., Simon-Chane, C., Histace A.,     
*[Evaluation of Event-Based Corner Detectors ](https://doi.org/10.3390/jimaging7020025)*,  
J. Imaging, 2021.
- <a name="Chiberre21cvprw"></a>Chiberre, P., Perot, E., Sironi, A., Lepetit, V.,  
*[Detecting Stable Keypoints From Events Through Image Gradient Prediction](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Chiberre_Detecting_Stable_Keypoints_From_Events_Through_Image_Gradient_Prediction_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [YouTube](https://youtu.be/Rrkwxp8J18c).
- <a name="Chui21arxiv"></a>Chui, J., Klenk, S., Cremers, D.,  
*[Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization](https://arxiv.org/pdf/2107.04536)*,  
arXiv preprint arXiv, 2021.
- <a name="Sengupta21ciss0"></a>Sengupta, J. P., Villemur, M., Andreou, A. G.,  
*[Efficient, event-driven feature extraction and unsupervised object tracking for embedded applications](https://doi.org/10.1109/CISS50987.2021.9400234)*,  
55th Annual Conf. on Information Sciences and Systems (CISS), 2021.
- [Alzugaray, I., Ph.D. Thesis, 2022](#Alzugaray22PhD),  
*Event-driven Feature Detection and Tracking for Visual SLAM*.
- <a name="Freeman23mmsys"></a>Freeman, A., Mayer-Patel, K., Singh, M.,  
*[Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems](https://doi.org/10.1145/3625468.3647618)*,  
ACM Multimedia Systems (MMSys), 2024. [PDF](https://arxiv.org/pdf/2312.08213.pdf), [Code](https://github.com/ac-freeman/adder-codec-rs).

<a name="particle-detection"></a>
### Particle Detection and Tracking
- <a name="Drazen11eif"></a>Drazen, D., Lichtsteiner, P., Haefliger, P., Delbruck, T., Jensen, A.,  
*[Toward real-time particle tracking using an event-based dynamic vision sensor](https://doi.org/10.1007/s00348-011-1207-y)*,  
Experiments in Fluids (2011), 51(1):1465-1469. [PDF](http://www.zora.uzh.ch/60624/1/Drazen_EIF_2011.pdf)
- <a name="Ni11jmcro"></a>Ni, Z., Pacoret, C., Benosman, R., Ieng, S., Regnier, S.,  
*[Asynchronous event-based high speed vision for microparticle tracking](http://doi.org/10.1111/j.1365-2818.2011.03565.x)*,  
J. Microscopy (2011), 245(3):236-244. [PDF](https://www.neuromorphic-vision.com/public/publications/14/publication.pdf)
- <a name="Borer14isfv"></a>Borer, D., Roesgen, T.,  
*[Large-scale Particle Tracking with Dynamic Vision Sensors](https://www.research-collection.ethz.ch/handle/20.500.11850/86729)*,  
ISFV16 - 16th Int. Symp. Flow Visualization, Okinawa 2014. [Project page](http://www.ifd.mavt.ethz.ch/research/group-roesgen/dynamic-vision-sensors.html), [Poster](http://www.ifd.mavt.ethz.ch/content/dam/ethz/special-interest/mavt/fluid-dynamics/ifd-dam/research/documents/posters/experimental-methods/daniel_borer_dynamic_vision_sensor.pdf)
- <a name="Wang20eccv"></a>Wang, Y., Idoughi, R., Heidrich, W.,  
*[Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740035.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740035-supp.zip)


<a name="eye_tracking"></a>
### Eye Tracking
- <a name="Ryan20arxiv"></a>Ryan, C., Sullivan, B. O., Elrasad, A., Lemley, J., Kielty., P., Posch, C., Perot, E.,  
*[Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://arxiv.org/pdf/2010.08278)*,  
arXiv, 2020.
- <a name="Angelopoulos20arxiv"></a>Angelopoulos, A.N., Martel, J.N.P., Kohli, A.P.S., Conradt, J., Wetzstein, G.,  
*[Event Based, Near-Eye Gaze Tracking Beyond 10,000Hz](https://arxiv.org/pdf/2004.03577)*,  
IEEE Trans. Vis. Comput. Graphics (Proc. VR), 2021. [YouTube](https://youtu.be/-7EneYIfinM), [Dataset](https://github.com/aangelopoulos/event_based_gaze_tracking), [Project page](http://www.computationalimaging.org/publications/event-based-eye-tracking/)
- <a name="Chen23biocas"></a>Chen, Q., Wang, Z., Liu, S.-C., Gao, C.,  
*[3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network](https://arxiv.org/abs/2308.11771)*,  
IEEE BioCAS Conf., 2023. [YouTube](https://www.youtube.com/watch?v=aRB5mDNfrHM), [Code](https://github.com/qinche106/cb-convlstm-eyetracking)
- <a name="Wang24cvprw"></a>Wang, Z., Gao, C., Wu, Z., Conde, M., Timofte, R., Liu, S.-C., Chen, Q., et al.  
*[Event-based Eye Tracking. AIS 2024 Challenge Survey](https://arxiv.org/abs/2404.11770)*,
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. [Challenge page](https://eetchallenge.github.io/EET.github.io/), [Code](https://github.com/EETChallenge/challenge_demo_code), [Kaggle page](https://www.kaggle.com/competitions/event-based-eye-tracking-ais2024)
- <a name="Bonazzi24cvprw"></a>Bonazzi, P., Bian, S., Lippolis, G., Sheik, S., Magno, M.  
*[Retina: Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://arxiv.org/pdf/2312.00425)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. [Dataset](https://pietrobonazzi.com/projects/retina), [Code](https://github.com/pbonazzi/retina), [PDF](https://arxiv.org/pdf/2312.00425).

<a name="optical-flow-estimation"></a>
## Optical Flow Estimation
- <a name="Benosman12neunet"></a>Delbruck, T.,  
*[Frame-free dynamic digital vision](https://www.research-collection.ethz.ch/handle/20.500.11850/81769),*  
Int. Symp. on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, pp. 21-26, 2008. [PDF](www.ini.uzh.ch/admin/extras/doc_get.php?id=42508)
- [Cook et al., IJCNN 2011](#Cook11ijcnn),  
*Interacting maps for fast visual interpretation*.  (Joint estimation of optical flow, image intensity and angular velocity with a rotating event camera).
- <a name="Benosman12neunet"></a>Benosman, R., Ieng, S.-H., Clercq, C., Bartolozzi, C., Srinivasan, M.,  
*[Asynchronous Frameless Event-Based Optical Flow](https://doi.org/10.1016/j.neunet.2011.11.001),*  
Neural Networks (2012), 27:32-37. [PDF](https://www.neuromorphic-vision.com/public/publications/12/publication.pdf), [Suppl. Mat.](https://www.neuromorphic-vision.com/public/publications/12/supplementaryMaterial.zip)
- <a name="Orchard13biocas"></a>Orchard, G., Benosman, R., Etienne-Cummings, R., Thakor, N,  
*[A Spiking Neural Network Architecture for Visual Motion Estimation](https://doi.org/10.1109/BioCAS.2013.6679698)*,  
IEEE Biomedical Circuits and Systems Conf. (BioCAS), 2013. [PDF](https://www.researchgate.net/publication/261075772_A_spiking_neural_network_architecture_for_visual_motion_estimation), [Code](https://github.com/gorchard/Spiking_Motion)
- <a name="Benosman14tnnls"></a>Benosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., Bartolozzi, C.,  
*[Event-Based Visual Flow](https://doi.org/10.1109/TNNLS.2013.2273537),*  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 25(2):407-417, 2014. [PDF](https://www.neuromorphic-vision.com/public/publications/3/publication.pdf), [Code (jAER): LocalPlanesFlow](https://github.com/SensorsINI/jaer/blob/master/src/ch/unizh/ini/jaer/projects/rbodo/opticalflow/LocalPlanesFlow.java)
    - [Clady et al., Front. Neurosci. 2014](#Clady14fnins),  
    *Asynchronous visual event-based time-to-contact*.
    - <a name="Mueggler15icra"></a>E. Mueggler, C. Forster, N. Baumli, G. Gallego, D. Scaramuzza,  
*[Lifetime Estimation of Events from Dynamic Vision Sensors](http://dx.doi.org/10.1109/ICRA.2015.7139876)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2015, pp. 4874-4881. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA15_Mueggler.pdf), [PPT](http://rpg.ifi.uzh.ch/docs/ICRA15_Mueggler.pptm), [Code](https://www.github.com/uzh-rpg/rpg_event_lifetime)
    - <a name="Lee17iccas"></a>Lee, A. J., Kim, A.,  
*[Event-based Real-time Optical Flow Estimation](https://doi.org/10.23919/ICCAS.2017.8204333)*,  
IEEE Int. Conf. on Control, Automation and Systems (ICCAS), 2017.
    - <a name="Aung18iscas"></a>Aung, M.T., Teo, R., Orchard, G.,  
*[Event-based Plane-fitting Optical Flow for Dynamic Vision Sensors in FPGA](https://doi.org/10.1109/ISCAS.2018.8351588)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2018. [Code](https://github.com/gorchard/FPGA_event_based_optical_flow)
- <a name="Barranco14ieee"></a>Barranco, F., Fermüller, C., Aloimonos, Y.,  
*[Contour motion estimation for asynchronous event-driven cameras](https://doi.org/10.1109/JPROC.2014.2347207)*,  
Proc. IEEE (2014), 102(10):1537-1556. [PDF](http://www.cfar.umd.edu/~fer/postscript/contourmotion-dvs-final.pdf)
- <a name="Lee14icip"></a>Lee, J.H., Lee, K., Ryu, H., Park, P.K.J., Shin, C.W., Woo, J., Kim, J.-S.,  
*[Real-time motion estimation based on event-based vision sensor](https://doi.org/10.1109/ICIP.2014.7025040)*,  
IEEE Int. Conf. Image Processing (ICIP), 2014.
- <a name="Richter14bc"></a>Richter, C., Röhrbein, F., Conradt, J.,  
[Bio inspired optic flow detection using neuromorphic hardware](https://doi.org/10.12751/NNCN.BC2014.0032),  
Bernstein Conf. 2014. [PDF](https://mediatum.ub.tum.de/doc/1281617/789727.pdf)
- <a name="Barranco15iwann"></a>Barranco, F., Fermüller, C., Aloimonos, Y.,  
*[Bio-inspired Motion Estimation with Event-Driven Sensors](https://doi.org/10.1007/978-3-319-19258-1_27)*,  
Int. Work-Conf. Artificial Neural Networks (IWANN) 2015, Advances in Computational Intell., pp. 309-321. [PDF](https://www.ugr.es/~fbarranco/docs/IWANN_Barranco_et_al_2015.pdf)
- <a name="Conradt15robio"></a>Conradt, J.,  
*[On-Board Real-Time Optic-Flow for Miniature Event-Based Vision Sensors](https://doi.org/10.1109/ROBIO.2015.7419043)*,  
IEEE Int. Conf. Robotics and Biomimetics (ROBIO), 2015.
- <a name="Brosch15fnins"></a>Brosch, T., Tschechne, S., Neumann, H.,  
*[On event-based optical flow detection](https://doi.org/10.3389/fnins.2015.00137)*,  
Front. Neurosci. (2015), 9:137.
    - <a name="Tschechne14bict"></a>Tschechne, S., Brosch, T., Sailer, R., von Egloffstein, N., Abdul-Kreem L.I., Neumann, H.,  
*[On event-based motion detection and integration](http://dx.doi.org/10.4108/icst.bict.2014.257904)*,  
Int. Conf. Bio-inspired Information and Comm. Technol. (BICT), 2014. [PDF](https://dl.acm.org/citation.cfm?id=2744588)
    - <a name="Tschechne14annpr"></a>Tschechne, S., Sailer R., Neumann, H.,  
*[Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input](https://doi.org/10.1007/978-3-319-11656-3_16)*,  
IAPR Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR) 2014, pp. 171-182.
    - <a name="Brosch15bict"></a>Brosch, T., Neumann, H.,  
*[Event-based optical flow on neuromorphic hardware](https://doi.org/10.4108/eai.3-12-2015.2262447)*,  
Int. Conf. Bio-inspired Information and Comm. Technol. (BICT), 2015. [PDF](https://dl.acm.org/citation.cfm?id=2954727)
    - <a name="Brosch15braincomp"></a>Brosch, T., Tschechne, S., Neumann, H.,  
*[Visual Processing in Cortical Architecture from Neuroscience to Neuromorphic Computing](https://doi.org/10.1007/978-3-319-50862-7_7)*,  
Int. Workshop on Brain-Inspired Computing (BrainComp), 2015. LNCS, vol 10087.
    - <a name="Kosiorek15techrep"></a>Kosiorek, A., Adrian, D., Rausch, J., Conradt, J.,  
*[An Efficient Event-Based Optical Flow Implementation in C/C++ and CUDA](http://tum.neurocomputing.systems/fileadmin/w00bqs/www/publications/pp/2015SS-PP-RealTimeDVSOpticFlow.pdf),*  
Tech. Rep. TU Munich, 2015.
- [Milde et al., EBCCSP 2015](#Milde15ebccsp),  
*Bioinspired event-driven collision avoidance algorithm based on optic flow*.
- <a name="Giulioni16fnins"></a>Giulioni, M., Lagorce, X., Galluppi, F., Benosman, R.,  
*[Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform](https://doi.org/10.3389/fnins.2016.00035)*,  
Front. Neurosci. (2016), 10:35. [PDF](https://www.neuromorphic-vision.com/public/publications/46/publication.pdf)
    - <a name="Haessig19aicas"></a>Haessig, G., Galluppi, F., Lagorce, X., Benosman, R.,  
*[Neuromorphic networks on the SpiNNaker platform](https://doi.org/10.1109/AICAS.2019.8771512)*,  
IEEE Int. Conf. Artificial Intelligence Circuits and Systems (AICAS), 2019.
- <a name="Rueckauer16fnins"></a>Rueckauer, B. and Delbruck, T.,  
*[Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor](https://doi.org/10.3389/fnins.2016.00176),*  
Front. Neurosci. (2016), 10:176. [YouTube](https://youtu.be/Ji1MzE4QbM4)
    - [Code (jAER)](https://github.com/SensorsINI/jaer/tree/master/src/ch/unizh/ini/jaer/projects/rbodo/opticalflow)
- <a name="Bardow16cvpr"></a>Bardow, P. A., Davison, A. J., Leutenegger, S.,  
*[Simultaneous Optical Flow and Intensity Estimation from an Event Camera](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Bardow_Simultaneous_Optical_Flow_CVPR_2016_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016. [YouTube](https://youtu.be/1zqJpiheaaI), [YouTube 2](https://youtu.be/CASsIFuPxmc), [Dataset: 4 sequences](http://wp.doc.ic.ac.uk/pb2114/datasets/)
- <a name="Stoffregen17acra"></a>Stoffregen, T., Kleeman, L.,  
*[Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor](http://www.araa.asn.au/acra/acra2017/papers/pap127s1-file1.pdf)*,  
Australasian Conf. Robotics and Automation (ACRA), 2017. [PDF](https://arxiv.org/pdf/1805.12326.pdf), [YouTube](https://youtu.be/JVkQOW_iUqs)
- <a name="Haessig17tbcas"></a>Haessig, G., Cassidy, A. Alvarez, R., Benosman, R., Orchard, G.,  
*[Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System](https://doi.org/10.1109/TBCAS.2018.2834558)*,  
IEEE Trans. Biomed. Circuits Syst., 12(4):860-870, 2018. [PDF](https://arxiv.org/pdf/1710.09820.pdf)
- [Gallego et al., CVPR 2018](#Gallego18cvpr),  
*A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation*.
    - <a name="Stoffregen19cvpr"></a>Stoffregen, T., Kleeman, L.,  
*[Event Cameras, Contrast Maximization and Reward Functions: An Analysis](http://openaccess.thecvf.com/content_CVPR_2019/html/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019.
    - [Gallego et al., CVPR 2019](#Gallego19cvpr),  
*Focus Is All You Need: Loss Functions For Event-based Vision*.
    - [Stoffregen et al., ICCV 2019](#Stoffregen19iccv),  
*Event-Based Motion Segmentation by Motion Compensation*.
    - [Ghosh et al., AISY 2022](#Ghosh22aisy),  
*Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion (MCEMVS)*.
    - [Shiba et al., Sensors 2022](#Shiba22sensors),  
*Event Collapse in Contrast Maximization Frameworks*.
    - [Shiba et al., AISY 2022](#Shiba22aisy),  
*A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework*.
    - [Shiba et al., ECCV 2022](#Shiba22eccv),  
*Secrets of Event-based Optical Flow*.
    - [Zhang et al., TPAMI 2023](#Zhang22tpami),  
*Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow*.
    - [Guo et al. TRO 2024](#Guo24tro),
*CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization*.
    - [Shiba et al. TPAMI 2024](#Shiba24tpami),  
*Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization*.
- <a name="Zhu18rss"></a>Zhu, A., Yuan, L., Chaney, K., Daniilidis, K.,  
*[EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](http://www.roboticsproceedings.org/rss14/p62.pdf)*,  
Robotics: Science and Systems (RSS), 2018. [PDF](https://arxiv.org/abs/1802.06898), [YouTube](https://youtu.be/eMHZBSoq0sE), [Code](https://github.com/daniilidis-group/EV-FlowNet)
    - [Gehrig et al., ICCV 2019](#Gehrig19iccv),  
*End-to-End Learning of Representations for Asynchronous Event-Based Data*.
- <a name="Liu18bmvc"></a>Liu, M., Delbruck, T.,  
*[Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors](http://bmvc2018.org/contents/papers/0280.pdf)*,  
British Machine Vision Conf. (BMVC), 2018. [Supplementary material](https://docs.google.com/document/d/10X0z4zznuV9j1OOjWpJGv-YCWujkF7FiYjG6efwUrP0/edit), [Video](https://youtu.be/fGJ8jyqziBI)
    - <a name="Liu17iscas"></a>Liu, M., Delbruck, T.,  
*[Block-Matching Optical Flow for Dynamic Vision Sensors: Algorithm and FPGA Implementation](https://arxiv.org/pdf/1706.05415.pdf)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2017.
- <a name="Ye20iros"></a>Ye, C., Mitrokhin, A., Parameshwara, C., Fermüller, C., Yorke, J. A., Aloimonos,Y,  
*[Unsupervised Learning of Dense Optical Flow, Depth and Egomotion with Event-Based Sensors](https://arxiv.org/pdf/1809.08625.pdf)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2020. [PDF](https://arxiv.org/pdf/1809.08625.pdf), [YouTube](https://youtu.be/hmDDAQVD0B0), [Project page](http://prg.cs.umd.edu/ECN.html)
- [Seifozzakerini, Ph.D. Thesis, 2018](#Seifozzakerini18PhD),  
*Analysis of object and its motion in event-based videos*.
- <a name="Nagata19iwait"></a>Nagata, J., Sekikawa, Y., Hara, K., Aoki, Y.,  
[FOE-based regularization for optical flow estimation from an in-vehicle event camera](https://doi.org/10.1117/12.2521520),  
Proc. SPIE 11049, Int. Workshop on Advanced Image Technology (IWAIT), 2019.
- <a name="ParedesValles19tpami"></a>Paredes-Valles, F., Scheper, K. Y. W., de Croon, G. C. H. E.,  
*[Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://ieeexplore.ieee.org/document/8660483)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2019. [PDF](https://arxiv.org/abs/1807.10936), [YouTube](https://www.youtube.com/watch?v=FJrba02kZII&list=PL_KSX9GOn2P80tm3IsgbmPAUi2KDE53zI&index=2&t=0s), [Code](https://github.com/tudelft/cuSNN).
- <a name="Zhu19cvpr"></a>Zhu, A. Z., Yuan, L., Chaney, K., Daniilidis, K.,  
*[Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Unsupervised_Event-Based_Learning_of_Optical_Flow_Depth_and_Egomotion_CVPR_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF](https://arxiv.org/pdf/1812.08156), [YouTube](https://youtu.be/aDzFSG4yV0M), [Patent](http://www.freepatentsonline.com/y2020/0265590.html)
    - <a name="Zhu18eccvw"></a>Zhu, A. Z., Yuan, L., Chaney, K., Daniilidis, K.,  
*[Unsupervised Event-Based Optical Flow Using Motion Compensation](https://doi.org/10.1007/978-3-030-11024-6_54)*,  
European Conf. Computer Vision Workshops (ECCVW), 2018. [PDF](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11134/Zhu_Unsupervised_Event-based_Optical_Flow_using_Motion_Compensation_ECCVW_2018_paper.pdf)
- <a name="Khoei19neco"></a>Khoei, M.A., Benosman, R.,  
*[Asynchronous Event-Based Motion Processing: From Visual Events to Probabilistic Sensory Representation](https://doi.org/10.1162/neco_a_01191)*,  
Neural Computation (2019), 31(6):1114-1138. [PDF](https://www.neuromorphic-vision.com/public/publications/56/publication.pdf)
- <a name="Almatrafi19davis"></a>Almatrafi, M. M., Hirakawa, K.,  
*[DAViS Camera Optical Flow](http://doi.org/10.1109/TCI.2019.2948787)*,  
IEEE Trans. Comput. Imag. (TCI), 6:396-407, 2019.
- <a name="Almatrafi20arxiv"></a>Almatrafi, M., Baldwin, R., Aizawa, K., Hirakawa, K.,  
*[Distance Surface for Event-Based Optical Flow](https://doi.org/10.1109/TPAMI.2020.2986748)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2020. [PDF](https://arxiv.org/pdf/2003.12680), [Dataset](https://sites.google.com/a/udayton.edu/issl/software/dataset?authuser=0)
- <a name="Lee20eccv"></a>Lee, C., Kosta, A., Zhu, A.Z., Chaney, K., Daniilidis, K., Roy, K.,  
*[Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740358.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740358-supp.pdf), [PDF](https://arxiv.org/pdf/2003.06696)
- <a name="DAngelo20fnins"></a>D'Angelo, G., Janotte, E., Schoepe, T., O'Keeffe, J., Milde, M. B., Chicca, E., Bartolozzi, C.,  
*[Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding](https://doi.org/10.3389/fnins.2020.00451)*,  
Front. Neurosci. (2020), 14:451. [Project page](https://github.com/event-driven-robotics/sEMD-iCub)
- <a name="Pan20cvpr"></a>Pan, L., Liu, M., Hartley, R.,  
*[Single Image Optical Flow Estimation with an Event Camera](http://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020.
- <a name="Low20cvprw"></a>Low, W. F., Gao, Z., Xiang, C., Ramesh, B.,  
[SOFEA: A Non-Iterative and Robust Optical Flow Estimation Algorithm for Dynamic Vision Sensors](http://openaccess.thecvf.com/content_CVPRW_2020/html/w6/Low_SOFEA_A_Non-Iterative_and_Robust_Optical_Flow_Estimation_Algorithm_for_CVPRW_2020_paper.html),  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2020. [PDF](http://openaccess.thecvf.com/content_CVPRW_2020/papers/w6/Low_SOFEA_A_Non-Iterative_and_Robust_Optical_Flow_Estimation_Algorithm_for_CVPRW_2020_paper.pdf), [Suppl. Mat.](http://openaccess.thecvf.com/content_CVPRW_2020/supplemental/Low_SOFEA_A_Non-Iterative_CVPRW_2020_supplemental.pdf)
- <a name="Akolkar20tpami"></a>Akolkar, H., Ieng, S.-H., Benosman, R.,  
*[Real-time high speed motion prediction using fast aperture-robust event-driven visual flow](https://doi.org/10.1109/TPAMI.2020.3010468)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2020. [PDF](https://arxiv.org/pdf/1811.11135)
- <a name="pivezhandi2020parahist"></a>Pivezhandi, M., Jones, P. H., Zambreno, J.,  
*[ParaHist: FPGA Implementation of Parallel Event-Based Histogram for Optical Flow Calculation](https://doi.org/10.1109/ASAP49362.2020.00038)*,  
IEEE Conf. Application-specific Systems, Architectures and Processors (ASAP), 2020. [PDF](http://rcl.ece.iastate.edu/sites/default/files/papers/PivJon20A.pdf)
- <a name="Kepple20eccv"></a>Kepple, D.R., Lee, D., Prepsius, C., Isler, V., Park, I. M., Lee, D. D.,  
*[Jointly learning visual motion and confidence from local patches in event cameras](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510494.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510494-supp.pdf)
- <a name="Nagata21sensors"></a>Nagata, J., Sekikawa, Y., Aoki, Y.,  
*[Optical Flow Estimation by Matching Time Surface with Event-Based Cameras](https://doi.org/10.3390/s21041150)*,  
Sensors 2021, 21, 1150. [PDF](https://www.mdpi.com/1424-8220/21/4/1150/pdf)
- [Paredes-Valles et al., CVPR 2021](#ParedesValles21cvpr),  
*Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy.*
- <a name="Hagenaars21neurips"></a>Hagenaars, J. J., Paredes-Valles, F., de Croon, G. C. H. E.,  
*[Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://proceedings.neurips.cc/paper/2021/hash/39d4b545fb02556829aab1db805021c3-Abstract.html)*,  
Advances in Neural Information Processing Systems 34 (NeurIPS), 2021. [Project page](https://mavlab.tudelft.nl/event_flow/), [PDF](https://proceedings.neurips.cc/paper/2021/file/39d4b545fb02556829aab1db805021c3-Paper.pdf), [Suppl. Mat.](https://proceedings.neurips.cc/paper/2021/file/39d4b545fb02556829aab1db805021c3-Supplemental.pdf), [Code](https://github.com/tudelft/event_flow).
- <a name="Sikorski21cvprw"></a>Sikorski, O., Izzo, D., Meoni, G.,  
*[Event-Based Spacecraft Landing Using Time-To-Contact](https://openaccess.thecvf.com/content/CVPR2021W/AI4Space/papers/Sikorski_Event-Based_Spacecraft_Landing_Using_Time-To-Contact_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
- <a name="Peveri21cvprw"></a>Peveri, F., Testa, S., Sabatini, S. P.,  
*[A Cortically-Inspired Architecture for Event-Based Visual Motion Processing: From Design Principles to Real-World Applications](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Peveri_A_Cortically-Inspired_Architecture_for_Event-Based_Visual_Motion_Processing_From_Design_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [YouTube](https://youtu.be/KyS_h8i9HpM).
- <a name="Barbier21cvprw"></a>Barbier, T., Teuliere, C., Triesch, J.,  
*[Spike Timing-Based Unsupervised Learning of Orientation, Disparity, and Motion Representations in a Spiking Neural Network](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Barbier_Spike_Timing-Based_Unsupervised_Learning_of_Orientation_Disparity_and_Motion_Representations_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Suppl.](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/supplemental/Barbier_Spike_Timing-Based_Unsupervised_CVPRW_2021_supplemental.pdf), [YouTube](https://youtu.be/TL567P70L68).
- <a name="Gehrig21threedv"></a>Gehrig, M., Millhäusler, M., Gehrig, D., Scaramuzza, D.,  
*[E-RAFT: Dense Optical Flow from Event Cameras](https://arxiv.org/pdf/2108.10552)*,  
IEEE Int. Conf. 3D Vision (3DV), 2021. [Code](https://github.com/uzh-rpg/E-RAFT), [Dataset](https://dsec.ifi.uzh.ch), [Youtube](https://youtu.be/dN8fl7-XfNw)
- <a name="Shiba22sensors"></a>Shiba, S., Aoki, Y., Gallego, G.,  
*[Event Collapse in Contrast Maximization Frameworks](https://www.mdpi.com/1424-8220/22/14/5190/htm)*,  
Sensors, 2022. [PDF](https://arxiv.org/pdf/2207.04007),  [Project page](https://github.com/tub-rip/event_collapse)
- <a name="Shiba24tpami"></a>Shiba, S., Klose, Y., Aoki, Y., Gallego, G.,  
*[Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization](https://doi.org/10.1109/TPAMI.2024.3396116)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2024. [Project page and Code](https://github.com/tub-rip/event_based_optical_flow)
    - <a name="Shiba22eccv"></a>Shiba, S., Aoki, Y., Gallego, G.,  
*[Secrets of Event-based Optical Flow](https://arxiv.org/pdf/2207.10022)*,  
Europen Conf. Computer Vision (ECCV), 2022. [YouTube](https://youtu.be/nUb2ZRPdbWk), [Poster](https://drive.google.com/file/d/1mF-mM4teb8A9bKJJiQwN7IFsGsRIsRaX/view?usp=sharing), [Presentation at  the  PRG  Seminar Series  U. Maryland (Video)](https://vid.umd.edu/detsmediasite/Play/d31926745bdd446e8f6ce165ae4811591d), [Presentation at the GRASP Laboratory (UPenn) seminar](https://youtu.be/8v-bYCVL9hw),
[Project page and Code](https://github.com/tub-rip/event_based_optical_flow)
- <a name="Shiba22spl"></a>Shiba, S., Aoki, Y., Gallego, G.,  
*[Fast Event-Based Optical Flow Estimation by Triplet Matching](https://doi.org/10.1109/LSP.2023.3234800)*,  
IEEE Signal Process. Lett. (SPL), 29:2712-2716, 2022. [PDF](https://arxiv.org/pdf/2212.12218).
- <a name="Brebion21tits"></a>Brebion, V., Moreau, J., Davoine, F.,  
*[Real-Time Optical Flow for Vehicular Perception With Low- and High-Resolution Event Cameras](https://doi.org/10.1109/TITS.2021.3136358)*,  
IEEE Trans. Intell. Transp. Syst. (T-ITS), 2021. [PDF](https://arxiv.org/pdf/2112.10591.pdf), [Code](https://github.com/vbrebion/rt_of_low_high_res_event_cameras), [Dataset](https://datasets.hds.utc.fr/share/er2aA4R0QMJzMyO), [YouTube](https://youtube.com/playlist?list=PLLL0eWAd6OXBRXli-tB1NREdhBElAxisD).
- <a name="Liu22tcsvt"></a>Liu, M., Delbruck, T.,  
*[EDFLOW: Event Driven Optical Flow Camera with Keypoint Detection and Adaptive Block Matching](https://doi.org/10.1109/TCSVT.2022.3156653)*,  
IEEE Trans. Circuits Syst. Video Technol. (TCSVT), 32(9):5776-5789, 2022. [Preprint PDF](https://drive.google.com/file/d/15YflL00x8X1StKwZqWR3YLBN8iHAP27E/view?usp=sharing), [Code and Dataset](https://sites.google.com/view/edflow21/home)
- <a name="Wan22tip"></a>Wan, Z., Dai, Y., Mao, Y.,  
*[Learning Dense and Continuous Optical Flow From an Event Camera](https://doi.org/10.1109/TIP.2022.3220938)*,  
IEEE Trans. Image Process. (TIP), 31:7237-7251, 2022. [PDF](https://arxiv.org/abs/2211.09078), [Project page](https://npucvr.github.io/DCEIFlow/), [Code](https://github.com/danqu130/DCEIFlow)
- <a name="Zheng22tip"></a>Zheng, Y., Yu, Z., Wang, S., Huang, T.,  
*[Spike-Based Motion Estimation for Object Tracking Through Bio-Inspired Unsupervised Learning](https://doi.org/10.1109/TIP.2022.3228168)*,  
IEEE Trans. Image Process. (TIP), 32:335-349, 2022.
- <a name="Gehrig22arxiv_flow"></a>Gehrig, M., Muglikar, M., Scaramuzza, D.,  
*[Dense Continuous-Time Optical Flow from Events and Frames](https://arxiv.org/abs/2203.13674)*,  
arXiv, 2022.  
- <a name="Tian22bmvc"></a>Tian, Y., Andrade-Cetto, J.,  
*[Event transformer FlowNet for optical flow estimation](https://bmvc2022.mpi-inf.mpg.de/577/)*,  
British Mach. Vis. Conf., 2022, [PDF](https://bmvc2022.mpi-inf.mpg.de/0577.pdf), [Poster](https://bmvc2022.mpi-inf.mpg.de/0577_poster.pdf), [Video](https://bmvc2022.mpi-inf.mpg.de/0577_video.mp4), [Supplementary](https://bmvc2022.mpi-inf.mpg.de/0577_supp.zip).
- [Shiba et al., TPAMI 2023](#Shiba23tpami),  
*Event-based Background-Oriented Schlieren*.


<a name="scene-flow-estimation"></a>
### Scene Flow Estimation
- <a name="Ieng17fnins"></a>Ieng, S.-H., Carneiro, J., Benosman, R.,  
*[Event-Based 3D Motion Flow Estimation Using 4D Spatio Temporal Subspaces Properties](https://doi.org/10.3389/fnins.2016.00596)*,  
Front. Neurosci. (2017), 10:596.
    - [Carneiro, Ph.D. Thesis, 2014](#Carneiro14PhD),  
*Asynchronous Event-Based 3D Vision - Chapter 3*.


<a name="visualization"></a>
## Reconstruction of Visual Information


<a name="image-reconstruction"></a>
### Intensity-Image Reconstruction from events
- <a name="Cook11ijcnn"></a>Cook, M., Gugelmann, L., Jug, F., Krautz, C., Steger, A.,  
*[Interacting maps for fast visual interpretation](https://doi.org/10.1109/IJCNN.2011.6033299)*,  
Int. Joint Conf. on Neural Networks (IJCNN), San Jose, CA, 2011, pp. 770-776. [PDF](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.690.7666&rep=rep1&type=pdf), [YouTube](https://youtu.be/irX3Nd5U0hY)
    - <a name="Martel15irosw"></a>Martel, J. N. P., Cook, M.,  
    *[A Framework of Relational Networks to Build Systems with Sensors able to Perform the Joint Approximate Inference of Quantities](https://doi.org/10.5167/uzh-121743)*,  
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems Workshop (IROSW), 2015. Workshop on Unconventional Computing for Bayesian Inference. [PDF](http://www.zora.uzh.ch/121743/1/RelationalNetSensor.pdf)
    - <a name="Martel15iscas"></a>Martel, J. N. P., Chau, M., Dudek, P., Cook, M.,  
    *[Toward joint approximate inference of visual quantities on cellular processor arrays](https://doi.org/10.1109/ISCAS.2015.7169083)*,  
    IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
- [Belbachir et al., CVPRW 2014](#Belbachir14cvprw),  
*A Novel HDR Depth Camera for Real-time 3D 360-degree Panoramic Vision*.
- <a name="Kim14bmvc"></a>Kim, H., Handa, A., Benosman, R., Ieng, S.-H., Davison, A. J.,  
*[Simultaneous Mosaicing and Tracking with an Event Camera](http://www.bmva.org/bmvc/2014/papers/paper066/)*,  
British Machine Vision Conf. (BMVC), 2014. [PDF](http://www.bmva.org/bmvc/2014/files/paper066.pdf), [YouTube](https://youtu.be/l6qxeM1DbXU), [YouTube 2](https://youtu.be/z72lNV7idUs)
    - [Code for intensity reconstruction](https://github.com/uzh-rpg/rpg_image_reconstruction_from_events).
    - [YouTube TU Graz](https://youtu.be/igsJHem8sFM) <!-- https://youtu.be/Ao6IoEqaBKc -->
- <a name="Barua16wacv"></a>Barua, S., Miyatani, Y., Veeraraghavan, A.,  
*[Direct face detection and video reconstruction from event cameras](http://doi.org/10.1109/WACV.2016.7477561)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2016. [YouTube](https://youtu.be/yGDVlN-L1TU)
- [Bardow et al., CVPR 2016](#Bardow16cvpr),  
*Simultaneous Optical Flow and Intensity Estimation from an Event Camera*.
- <a name="Moeys17iscas"></a>Moeys, D. P., Li, C., Martel, J. N. P., Bamford, S., Longinotti, L., Motsnyi, V., Bello, D. S. S., Delbruck, T.,  
*[Color Temporal Contrast Sensitivity in Dynamic Vision Sensors](http://dx.doi.org/10.1109/ISCAS.2017.8050412)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2017. [PDF](http://www.ini.uzh.ch/admin/extras/doc_get.php?id=65634).
- <a name="Munda18ijcv"></a>Munda, G., Reinbacher, C., Pock, T.,  
*[Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://doi.org/10.1007/s11263-018-1106-2)*,  
Int. J. of Computer Vision (IJCV), 2018.
    - <a name="Reinbacher16bmvc"></a>Reinbacher, C., Graber, G., Pock, T.,  
*[Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](http://www.bmva.org/bmvc/2016/papers/paper009/)*,  
British Machine Vision Conf. (BMVC), 2016. [PDF](http://www.bmva.org/bmvc/2016/papers/paper009/paper009.pdf), [YouTube](https://youtu.be/rvB2URrGT94), [Code](https://github.com/VLOGroup/dvs-reconstruction)
- <a name="Watkins18icons"></a>Watkins, Y., Thresher, A., Mascarenas, D., Kenyon,  G.T.,  
*[Sparse Coding Enables the Reconstruction of High-Fidelity Images and Video from Retinal Spike Trains](https://doi.org/10.1145/3229884.3229892)*,  
Int. Conf. Neuromorphic Systems (ICONS), 2018. Article No. 8. [PDF](https://dl.acm.org/ft_gateway.cfm?id=3229892&ftid=1990359&dwn=1&CFID=41965358&CFTOKEN=fe90e12f8b0b321e-23C508A3-0508-BCD6-37A17F6C8E171DAB)
- <a name="Scheerlinck18accv"></a>Scheerlinck, C., Barnes, N., Mahony, R.,  
*[Continuous-time Intensity Estimation Using Event Cameras](https://cedric-scheerlinck.github.io/files/2018_scheerlinck_continuous-time_intensity_estimation.pdf)*,  
Asian Conf. Computer Vision (ACCV), 2018. [PDF](https://cedric-scheerlinck.github.io/files/2018_scheerlinck_continuous-time_intensity_estimation.pdf), [YouTube](https://youtu.be/bZ0ZKido0Ag), [Website](https://cedric-scheerlinck.github.io/continuous-time-intensity-estimation)
- <a name="Rebecq20tpami"></a>Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.,  
*[High Speed and High Dynamic Range Video with an Event Camera](https://doi.org/10.1109/TPAMI.2019.2963386)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2020. [PDF](http://rpg.ifi.uzh.ch/docs/TPAMI19_Rebecq.pdf),  [YouTube](https://youtu.be/eomALySSGVU), [Code](https://github.com/uzh-rpg/rpg_e2vid), [Project page](http://rpg.ifi.uzh.ch/E2VID/)
    - <a name="Rebecq19cvpr"></a>Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.,  
*[Events-to-Video: Bringing Modern Computer Vision to Event Cameras](http://rpg.ifi.uzh.ch/docs/CVPR19_Rebecq.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF](http://rpg.ifi.uzh.ch/docs/CVPR19_Rebecq.pdf),  [YouTube](https://youtu.be/IdYrC4cUO0I), [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Henri_Rebecq.pdf), [Video pitch](https://youtu.be/1LZKtnQ-6lA).
    - <a name="Scheerlinck20wacv"></a>Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R., Scaramuzza, D.,  
*[Fast Image Reconstruction with an Event Camera](http://openaccess.thecvf.com/content_WACV_2020/papers/Scheerlinck_Fast_Image_Reconstruction_with_an_Event_Camera_WACV_2020_paper.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2020. [PDF](https://cedric-scheerlinck.github.io/files/2019_firenet.pdf),  [YouTube](https://youtu.be/QaGYFtR2-X8), [Website](https://cedric-scheerlinck.github.io/firenet)  
    - <a name="Stoffregen20eccv"></a>Stoffregen, T., Scheerlinck, C., Scaramuzza, D., Drummond, T., Barnes, N., Kleeman, L., Mahony, R.,  
*[Reducing the Sim-to-Real Gap for Event Cameras](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720528.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [PDF](https://arxiv.org/pdf/2003.09078), [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720528-supp.zip), [YouTube](https://youtu.be/m0brrMtrKoc), [Project page](https://timostoff.github.io/20ecnn)
- <a name="Mostafavi21ijcv"></a>Mostafavi, M., Wang, L., Yoon, K.J.,  
*[Learning to Reconstruct HDR Images from Events, with Applications to Depth and Flow Prediction](https://doi.org/10.1007/s11263-020-01410-2)*,  
Int. J. Computer Vision (IJCV), 2021.
    - <a name="Mostafavi19cvpr"></a>Mostafavi I., S.M., Wang, L., Ho, Y.S., Yoon, K.J.,  
*[Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Event-Based_High_Dynamic_Range_Image_and_Very_High_Frame_Rate_CVPR_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF](https://arxiv.org/abs/1811.08230)
- [Scheerlinck et al., CVPRW 2019](#Scheerlinck19cvprw),  
*CED: Color Event Camera Dataset*.
- <a name="Nagata20wacv"></a>Nagata, J., Sekikawa, Y., Hara, K., Suzuki, T., Aoki, Y.,  
*[QR-code Reconstruction from Event Data via Optimization in Code Subspace](http://openaccess.thecvf.com/content_WACV_2020/papers/Nagata_QR-code_Reconstruction_from_Event_Data_via_Optimization_in_Code_Subspace_WACV_2020_paper.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2020.
- <a name="Zhang20eccv"></a>Zhang, S., Zhang, Y., Jiang, Z., Zou, D., Ren, J., Zhou, B.,  
*[Learning to See in the Dark with Events](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630647.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630647-supp.pdf)
- <a name="Su20icip"></a>Su, B., Yu, L., Yang, W.,  
*[Event-Based High Frame-Rate Video Reconstruction With A Novel Cycle-Event Network](https://doi.org/10.1109/ICIP40778.2020.9191114)*,  
IEEE Int. Conf. Image Processing (ICIP), 2020.
- <a name="GantierCadena21tip"></a>Gantier Cadena, P. R., Qian, Y., Wang, C., Yang, M.,  
*[SPADE-E2VID: Spatially-Adaptive Denormalization for Event-Based Video Reconstruction](https://doi.org/10.1109/TIP.2021.3052070)*,  
IEEE Trans. Image Process. (TIP), 30:2488-2500, 2021. [Project page](https://github.com/RodrigoGantier/SPADE_E2VID)
- [Baldwin et al., TPAMI 2022](#Baldwin22tpami).  
*Time-Ordered Recent Event (TORE) Volumes for Event Cameras*.
- <a name="ParedesValles21cvpr"></a>Paredes-Valles, F., de Croon, G. C. H. E.,  
*[Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://openaccess.thecvf.com/content/CVPR2021/papers/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Project page](http://mavlab.tudelft.nl/ssl_e2v/), [PDF](https://arxiv.org/abs/2009.08283), [Suppl. Mat.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Paredes-Valles_Back_to_Event_CVPR_2021_supplemental.pdf), [Code](https://github.com/tudelft/ssl_e2vid).
- <a name="Zou21cvpr"></a>Zou, Y., Zheng, Y., Takatani, T., Fu, Y.,  
*[Learning To Reconstruct High Speed and High Dynamic Range Videos From Events](https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021.
- <a name="Yu22tpami"></a>  Yu, L., Zhang, X., Liao, W., Yang, W., Xia, G.-S.,  
*[Learning to See Through with Events](https://doi.org/10.1109/TPAMI.2022.3227448)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2022. [PDF](https://arxiv.org/pdf/2212.02219), [Project page](https://dvs-whu.cn/projects/esai/), [Dataset](https://drive.google.com/file/d/19SnoRfVgvKlUspAyzbBhtXszXkh5NsbS/view?usp=sharing)
    - <a name="Zhang21cvpr"></a>Zhang, X., Liao, W., Yu, L., Yang, W., Xia, G.-S.,  
*[Event-Based Synthetic Aperture Imaging With a Hybrid Network](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Suppl.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Event-Based_Synthetic_Aperture_CVPR_2021_supplemental.pdf), [PDF](http://arxiv.org/abs/2103.02376), [YouTube](https://youtu.be/vom7qcZ7t9Y), [Slides](https://v.youku.com/v_show/id_XNTE3MzczMzc2MA==.html)
- <a name="Li22mir"></a>Li, S., Gao, Y., Dai, Q.,  
*[Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network](https://link.springer.com/article/10.1007/s11633-022-1350-3)*,  
Machine Intelligence Research, 2022. [Code](https://github.com/lisiqi19971013/Event_Enhanced_DeOcc), [Dataset](https://github.com/lisiqi19971013/event-based-datasets).
- <a name="CohenDuwek21cvprw"></a>Cohen Duwek, H., Shalumov, A., Ezra Tsur, E.,  
*[Image Reconstruction From Neuromorphic Event Cameras Using Laplacian-Prediction and Poisson Integration With Spiking and Artificial Neural Networks](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Duwek_Image_Reconstruction_From_Neuromorphic_Event_Cameras_Using_Laplacian-Prediction_and_Poisson_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [YouTube](https://youtu.be/CoocgoUhxvU).
- <a name="Zhang22tpami"></a>Zhang, Z., Yezzi, A., Gallego, G.,  
*[Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://doi.org/10.1109/TPAMI.2022.3230727)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2022. [PDF](https://arxiv.org/pdf/2112.06242),  [Code](https://github.com/tub-rip/event_based_image_rec_inverse_problem)
- <a name="Zhu2022cvpr"></a>Zhu, L., Wang, X., Chang, Y., Li, J., Huang T., Tian Y,  
*[Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [Project page](https://sites.google.com/view/evsnn), [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.pdf), [Suppl. Mat.](https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Event-Based_Video_Reconstruction_CVPR_2022_supplemental.pdf), [Code](https://github.com/LinZhu111/EVSNN).
- <a name="Liu23tpami"></a>Liu, S., Dragotti, P.L.,    
*[Sensing Diversity and Sparsity Models for Event Generation and Video Reconstruction from Events](https://doi.org/10.1109/TPAMI.2023.3278940)*,    
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2023. [Code](https://github.com/pld-group/V2E2V), [Code for data generation](https://github.com/lsying009/V2E_Generation),  [Code for evaluation](https://github.com/lsying009/CISTA-EVREAL)
    - <a name="Liu22icassp"></a>Liu, S., Alexandru, R., Dragotti, P.L.,    
      *[Convolutional ISTA Network with Temporal Consistency Constraints for Video Reconstruction from Event Cameras](https://doi.org/10.1109/ICASSP43922.2022.9746331)*,    
      IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), 2022. [Code](https://github.com/pld-group/V2E2V)
    - <a name="Liu24arxiv"></a>Liu, S., Dragotti, P.L.,    
      *[Enhanced Event-Based Video Reconstruction with Motion Compensation](https://arxiv.org/pdf/2403.11961)*,    
      arxiv, 2024. [Code](https://github.com/lsying009/CISTA-Flow)
- <a name="Qu24aaai"></a>Qu, Q., Shen, Y., Chen, X., Chung, Y.Y., Liu, T.,    
*[E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning](https://arxiv.org/abs/2401.08117)*,    
AAAI Conf. Artificial Intelligence (AAAI), 2024. [Code](https://github.com/VincentQQu/E2HQV)
- <a name="Ercan24tip"></a>Ercan, B., Eker, O., Saglam, C., Erdem, A., Erdem, E.,  
*[HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://doi.org/10.1109/TIP.2024.3372460)*,  
IEEE Trans. Image Process. (TIP), 33:1826-1837, 2024. [PDF](https://arxiv.org/pdf/2305.06382), [Project Page](https://ercanburak.github.io/HyperE2VID.html), [Code](https://github.com/ercanburak/HyperE2VID), [YouTube](https://www.youtube.com/watch?v=BWEV56-E0mE).
- <a name="Zhang24tip"></a>Zhang, P., Liu, H., Ge, Z., Wang, C., Lam, E. Y.,   
*[Neuromorphic Imaging with Joint Image Deblurring and Event Denoising](https://doi.org/10.1109/TIP.2024.3374074)*,  
IEEE Trans. Image Process. (TIP), 2024. [PDF](https://arxiv.org/pdf/2309.16106.pdf), [Code](https://github.com/pz-even/ni-joint)

<a name="video-synthesis"></a>
### Video Synthesis
- <a name="Brandli14iscas"></a>Brandli, C., Muller, L., Delbruck, T.,  
*[Real-time, high-speed video decompression using a frame- and event-based DAVIS sensor](https://doi.org/10.1109/ISCAS.2014.6865228)*,  
IEEE Int. Symp. on Circuits and Systems (ISCAS), 2014.
    - [Brandli, Ph.D. Thesis, 2014](#Brandli15PhD),  
*Event-Based Machine Vision - Section 4.11*.
- <a name="Liu17tvc"></a>Liu HC., Zhang FL., Marshall D., Shi L., Hu SM.,  
*[High-speed Video Generation with an Event Camera](https://link.springer.com/article/10.1007/s00371-017-1372-y)*,  
The Visual Computer, 2017. [PDF](https://cg.cs.tsinghua.edu.cn/papers/TVC-2017-HS-Video.pdf).
- <a name="Shedligeri19jei"></a>Shedligeri, P.A., Mitra, K.,  
*[Photorealistic Image Reconstruction from Hybrid Intensity and Event based Sensor](https://doi.org/10.1117/1.JEI.28.6.063012)*,  
J. Electronic Imaging, 28(6), 063012 (2019). [PDF](https://arxiv.org/pdf/1805.06140.pdf)
- <a name="Wang19iccvw"></a>Wang, Z. W., Jiang, W., He, K., Shi, B., Katsaggelos, A., Cossairt, O.,  
*[Event-driven Video Frame Synthesis](http://openaccess.thecvf.com/content_ICCVW_2019/papers/PBDL/Wang_Event-Driven_Video_Frame_Synthesis_ICCVW_2019_paper.pdf)*,  
IEEE Int. Conf. Computer Vision Workshops (ICCVW), 2019. [PDF](https://arxiv.org/abs/1902.09680)
- <a name="Pan19cvpr"></a>Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., Dai, Y.,  
*[Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](http://openaccess.thecvf.com/content_CVPR_2019/papers/Pan_Bringing_a_Blurry_Frame_Alive_at_High_Frame-Rate_With_an_CVPR_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF](https://arxiv.org/pdf/1811.10180). [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Yuchao_Dai.pdf), [Video CVPR](https://drive.google.com/file/d/1NscnUF2QxK0of4ZW7T8kneJTH1X76l2u/view), [Video CVPRW](https://youtu.be/JcgboJ_7JAE), [Code](https://github.com/panpanfei/Bringing-a-Blurry-Frame-Alive-at-High-Frame-Rate-with-an-Event-Camera)
    - <a name="Pan19arxiv"></a>Pan, L., Hartley, R., Scheerlinck, C., Liu, M., Yu, X., Dai, Y.,  
*[High Frame Rate Video Reconstruction based on an Event Camera](https://arxiv.org/pdf/1903.06531)*,  
arXiv, 2019.
    - Open-source Rust implementation: [davis-EDI-rs](https://github.com/ac-freeman/davis-EDI-rs)
- <a name="Pini19iciap"></a>Pini, S., Borghi, G., Vezzani, R., Cucchiara, R.,  
*[Video Synthesis from Intensity and Event Frames](https://doi.org/10.1007/978-3-030-30642-7_28)*,  
Int. Conf. Image Analysis and Processing (ICIAP), 2019. LNCS, vol 11751. [PDF](https://iris.unimore.it/retrieve/handle/11380/1178955/224434/ICIAP19_Event_Cameras.pdf)
- <a name="Pini20visapp"></a>Pini S., Borghi G., Vezzani R.,  
*[Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras](http://hdl.handle.net/11380/1185831)*,  
Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) 2020. [PDF](https://arxiv.org/pdf/1812.02041)
- <a name="Haoyu20arxiv"></a>Haoyu, C., Minggui, T., Boxin, S., Yizhou, W., Tiejun, H.,  
*[Learning to Deblur and Generate High Frame Rate Video with an Event Camera](https://arxiv.org/pdf/2003.00847)*,  
arXiv:2003.00847, 2020.
- <a name="Jiang20cvpr"></a>Jiang, Z., Zhang, Y., Zou, D., Ren, J., Lv, J., Liu, Y.,  
*[Learning Event-Based Motion Deblurring](http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Learning_Event-Based_Motion_Deblurring_CVPR_2020_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020.
- <a name="WangB20eccv"></a>Wang, B., He, J., Yu, L., Xia, G.-S., Yang, W.,  
*[Event Enhanced High-Quality Image Recovery](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580154.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580154-supp.pdf), [Video](https://www.bilibili.com/video/av838383543/)
- <a name="Lin20eccv"></a>Lin, S., Zhang, J., Pan, J., Jiang, Z., Zou, D., Wang, Y., Chen, J., Ren, J.,  
*[Learning Event-Driven Video Deblurring and Interpolation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530681.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530681-supp.pdf)
- <a name="Zhang20access"></a>Zhang, L., Zhang, H., Chen, J., Wang, L.,  
*[Hybrid Deblur Net: Deep Non-Uniform Deblurring With Event Camera](https://doi.org/10.1109/ACCESS.2020.3015759)*,  
IEEE Access, 8:148075-148083, 2020.
- <a name="Jiang20icip"></a>Jiang, M., Liu, Z., Wang, B., Yu, L., Yang, W.,  
*[Robust Intensity Image Reconstruciton Based On Event Cameras](https://doi.org/10.1109/ICIP40778.2020.9190830)*,  
IEEE Int. Conf. Image Processing (ICIP), 2020.
- <a name="Zhang21mmm"></a>Zhang, L., Zhang, H., Zhu, C., Guo, S., Chen, J., Wang, L.,  
*[Fine-Grained Video Deblurring with Event Camera](https://doi.org/10.1007/978-3-030-67832-6_29)*,  
MultiMedia Modeling (MMM) 2021. LNCS, vol 12572.
- <a name="Tulyakov21cvpr"></a>Tulyakov, S., Gehrig, D., Georgoulis, S., Erbach, J., Gehrig, M., Li, Y., Scaramuzza, D.,  
*[Time Lens: Event-Based Video Frame Interpolation](https://openaccess.thecvf.com/content/CVPR2021/papers/Tulyakov_Time_Lens_Event-Based_Video_Frame_Interpolation_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Project page](http://rpg.ifi.uzh.ch/TimeLens.html), [Suppl.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tulyakov_Time_Lens_Event-Based_CVPR_2021_supplemental.zip), [PDF](http://rpg.ifi.uzh.ch/docs/CVPR21_Gehrig.pdf), [YouTube](https://youtu.be/dVLyia-ezvo), [Slides](http://rpg.ifi.uzh.ch/timelens/slides.pdf), [Code](https://github.com/uzh-rpg/rpg_timelens)
- <a name="Paikin21cvprw"></a>Paikin, G., Ater, Y., Shaul, R., Soloveichik, E.,  
*[EFI-Net: Video Frame Interpolation from Fusion of Events and Frames](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Paikin_EFI-Net_Video_Frame_Interpolation_From_Fusion_of_Events_and_Frames_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [YouTube](https://youtu.be/hrfsDOL18pw), [Suppl.](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/supplemental/Paikin_EFI-Net_Video_Frame_CVPRW_2021_supplemental.zip), [Dataset](https://drive.google.com/file/d/1UIGVBqNER_5KguYPAu5y7TVg-JlNhz3-/view?usp=sharing).
- <a name="Sun22eccv"></a>Sun, L., Sakaridis, C., Liang, J., Jiang, Q., Yang, K., Sun, P., Ye, Y., Wang, K., Gool, L.,  
*[Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/3239_ECCV_2022_paper.php)*,  
European Conf. Computer Vision (ECCV), 2022. [PDF](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780403.pdf), [Code](https://github.com/AHupuJR/EFNet), [Suppl.](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780403-supp.pdf), [Project page](https://ahupujr.github.io/EFNet/)
- <a name="Chen22tmm"></a>Chen, H., Teng, M., Shi, B., Wang, Y., Huang, T.,  
*[A Residual Learning Approach to Deblur and Generate High Frame Rate Video With an Event Camera](https://doi.org/10.1109/TMM.2022.3199556)*,  
IEEE Trans. Multimedia (TMM), 2022. [PDF](https://ci.idm.pku.edu.cn/Chen_TMM22.pdf)
- <a name="Gao22tpami"></a>Gao, Y., Li, S., Li, Y., Guo, Y., Dai, Q.,  
*[SuperFast: 200x Video Frame Interpolation via Event Camera](https://doi.org/10.1109/TPAMI.2022.3224051)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2022. [Code](https://github.com/lisiqi19971013/SuperFast), [Dataset](https://github.com/lisiqi19971013/event-based-datasets).
- <a name="Xiao22tci"></a>Xiao, Z., Weng, W., Zhang, Y., Xiong, Z.,  
*[EVA2: Event-Assisted Video Frame Interpolation via Cross-Modal Alignment and Aggregation](https://doi.org/10.1109/TCI.2022.3228747)*,  
IEEE Trans. Comput. Imag. (TCI), 8:1145-1158, 2022.
- <a name="Tulyakov22cvpr"></a>Tulyakov, S., Bochicchio, A., Gehrig, D., Georgoulis, S., Li, Y., Scaramuzza, D.,  
*[Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](http://dx.doi.org/10.1109/CVPR52688.2022.01723)*,  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR22_Tulyakov.pdf), [dataset](https://github.com/uzh-rpg/timelens-pp/), [YouTube](https://youtu.be/bWJz_p8Y5Hc).  
- <a name="Sun23cvpr"></a>Sun, L., Sakaridis, C., Liang, J., Sun, P., Cao, J., Zhang, K., Jiang, Q., Wang, K., Gool, L.,  
*[Event-Based Frame Interpolation with Ad-hoc Deblurring](https://arxiv.org/abs/2301.05191)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [Code](https://github.com/AHupuJR/REFID), [Project page](https://ahupujr.github.io/REFID/)
- <a name="Freeman23mmsys"></a>Freeman, A., Singh, M., Mayer-Patel, K.,  
*[An Asynchronous Intensity Representation for Framed and Event Video Sources](https://doi.org/10.1145/3587819.3590969)*,  
ACM Multimedia Systems (MMSys), 2023. [PDF](https://arxiv.org/abs/2301.08783), [Code](https://github.com/ac-freeman/adder-codec-rs/).
- <a name="Wang23tpami"></a>Wang, Z., Ng, Y., Scheerlinck, C., Mahony., R.,  
*[An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://doi.org/10.1109/TPAMI.2023.3311534)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2023. [PDF](https://arxiv.org/pdf/2309.01159.pdf), [Project Page](https://github.com/ziweiWWANG/Event-Asynchronous-Filter), [YouTube](https://www.youtube.com/watch?v=K1kRjw9xQNU).
    - <a name="Wang21iccv"></a>Wang, Z., Ng, Y., Scheerlinck, C., Mahony., R.,  
*[An Asynchronous Kalman Filter for Hybrid Event Cameras](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.html)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [PDF](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.pdf), [Code](https://github.com/ziweiWWANG/AKF), [YouTube](https://www.youtube.com/watch?v=XPz7laloKws), [Suppl](https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_An_Asynchronous_Kalman_ICCV_2021_supplemental.pdf).
- <a name="Wang23arxivb"></a>Wang, Z., Hamann, F., Chaney, K., Jiang, W., Gallego, G., Daniilidis, K.,  
*[Event-based Continuous Color Video Decompression from Single Frames](https://arxiv.org/pdf/2312.00113.pdf)*,  
arxiv, 2023. [Project page](https://www.cis.upenn.edu/~ziyunw/continuity_cam/)
<a name="super-resolution"></a>
- <a name="Chen23IROS"></a>Chen, J., Zhu, Y., Lian, D., Yang, J., Wang, Y., Zhang, R., Liu, X., Qian, S., Kneip, L., Gao, S.,  
*[Revisiting Event-Based Video Frame Interpolation](https://ieeexplore.ieee.org/abstract/document/10341804)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2023. [PDF](https://arxiv.org/pdf/2307.12558.pdf), [Project](https://jiabenchen.github.io/revisit_event)
- [Qi et al., ICCV 2023](#Qi23iccv),  
*E<sup>2</sup>NeRF: Event Enhanced Neural Radiance Fields from Blurry Images*.

### Image super-resolution
- <a name="Li19neucom"></a>Li, H., Li, G., Shi, L.,  
*[Super-resolution of spatiotemporal event-stream image](https://doi.org/10.1016/j.neucom.2018.12.048)*,  
Neurocomputing, 335:206-214, 2019. [PDF pre-print](https://arxiv.org/abs/1802.02398)
- <a name="Mostafavi20cvpr"></a>Mostafavi I., S.M.,  Choi, J., Yoon, K.-J.,  
*[Learning to Super Resolve Intensity Images from Events](http://openaccess.thecvf.com/content_CVPR_2020/papers/I._Learning_to_Super_Resolve_Intensity_Images_From_Events_CVPR_2020_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [PDF](https://arxiv.org/abs/1912.01196), [Code](https://github.com/gistvision/e2sri)
- <a name="Wang20cvpr"></a>Wang, L., Kim, T.-K., Yoon, K.-J.,  
*[EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_EventSR_From_Asynchronous_Events_to_Image_Reconstruction_Restoration_and_Super-Resolution_CVPR_2020_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [PDF](https://arxiv.org/pdf/2003.07640), [YouTube](https://youtu.be/OShS_MwHecs), [Dataset](https://github.com/wl082013/ESIM_dataset)
- <a name="Jing21cvpr"></a>Jing, Y., Yang, Y., Wang, X., Song, M., Tao, D.,  
*[Turning Frequency to Resolution: Video Super-Resolution via Event Cameras](https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021.
- <a name="Duan21cvpr"></a>Duan, P., Wang, Z. W., Zhou, X., Ma, Y., Shi, B.,  
*[EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events](https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_EventZoom_Learning_To_Denoise_and_Super_Resolve_Neuromorphic_Events_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Project page](https://sites.google.com/view/EventZoom), [Suppl.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_EventZoom_Learning_To_CVPR_2021_supplemental.zip)
- <a name="Han21iccv"></a>Han, J., Yang, Y., Zhou, C., Xu, C., Shi, B.,  
*[EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-resolution](https://openaccess.thecvf.com/content/ICCV2021/papers/Han_EvIntSR-Net_Event_Guided_Multiple_Latent_Frames_Reconstruction_and_Super-Resolution_ICCV_2021_paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [PDF](https://openaccess.thecvf.com/content/ICCV2021/papers/Han_EvIntSR-Net_Event_Guided_Multiple_Latent_Frames_Reconstruction_and_Super-Resolution_ICCV_2021_paper.pdf), [Suppl.](https://openaccess.thecvf.com/content/ICCV2021/supplemental/Han_EvIntSR-Net_Event_Guided_ICCV_2021_supplemental.pdf)
- <a name="Li21iccv"></a>Li, S., Feng, Y., Li, Y., Jiang, Y., Zou, C., Gao, Y.,  
*[Event Stream Super-Resolution via Spatiotemporal Constraint Learning](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Event_Stream_Super-Resolution_via_Spatiotemporal_Constraint_Learning_ICCV_2021_paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [Code](https://github.com/lisiqi19971013/EventStream-SR), [Dataset](https://github.com/lisiqi19971013/event-based-datasets).
- <a name="Kai23icip"></a>Kai, D., Zhang, Y., Sun, X.,  
*[Video Super-Resolution Via Event-Driven Temporal Alignment](https://doi.org/10.1109/ICIP49359.2023.10222922)*,  
IEEE Int. Conf. on Image Processing (ICIP), 2023. [Code](https://github.com/DachunKai/EBVSR).
- <a name="Kai24icml"></a>Kai, D., Lu, J., Zhang, Y., Sun, X.,  
*[EvTexture: Event-driven Texture Enhancement for Video Super-Resolution](https://arxiv.org/pdf/2406.13457)*,  
Int. Conf. on Machine Learning (ICML), 2024. [Project](https://dachunkai.github.io/evtexture.github.io/), [Code](https://github.com/DachunKai/EvTexture).

<a name="joint-filtering"></a>
### Joint/guided filtering
- <a name="Wang20cvpr-gef"></a>Wang, Z. W., Duan, P., Cossairt, O., Katsaggelos, A., Huang, T., Shi, B.,  
*[Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging](https://compphotolab.northwestern.edu/wordpress/wp-content/uploads/2020/04/cvpr2020-gef-compressed.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [YouTube](https://youtu.be/JpJFBZ97KX0), [Dataset](https://sites.google.com/view/guided-event-filtering)  


<a name="tone-mapping"></a>
### Tone Mapping
- <a name="SimonChane16fnins"></a>Simon Chane, C., Ieng, S.-H., Posch, C., Benosman, R.,  
*[Event-Based Tone Mapping for Asynchronous Time-Based Image Sensor](https://doi.org/10.3389/fnins.2016.00391)*,  
Front. Neurosci. (2016), 10:391. [PDF](https://www.neuromorphic-vision.com/public/publications/43/publication.pdf)
- <a name="Han20cvpr"></a>Han, J., Zhou, C., Duan, P., Tang, Y., Xu, C., Xu, C., Huang, T., Shi, B.,  
*[Neuromorphic Camera Guided High Dynamic Range Imaging](http://openaccess.thecvf.com/content_CVPR_2020/html/Han_Neuromorphic_Camera_Guided_High_Dynamic_Range_Imaging_CVPR_2020_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [PDF](http://openaccess.thecvf.com/content_CVPR_2020/papers/Han_Neuromorphic_Camera_Guided_High_Dynamic_Range_Imaging_CVPR_2020_paper.pdf), [Suppl. Mat.](http://openaccess.thecvf.com/content_CVPR_2020/supplemental/Han_Neuromorphic_Camera_Guided_CVPR_2020_supplemental.pdf)
- <a name="Messikommer22cvprw"></a>Messikommer, N., Georgoulis, S., Gehrig, D., Tulyakov, S., Erbach, J., Bochicchio, A., Li, Y., Scaramuzza, D.,  
*[Multi-Bracket High Dynamic Range Imaging with Event Cameras](http://dx.doi.org/10.1109/CVPRW56347.2022.00070)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2022. [PDF](https://rpg.ifi.uzh.ch/docs/CVPRW22_Messikommer.pdf), [YouTube](https://youtu.be/fw9-gNg6cM8).  

<a name="visual-stabilization"></a>
### Visual Stabilization
- <a name="Delbruck14iscas"></a>Delbruck, T., Villanueva, V., Longinotti, L.,  
*[Integration of dynamic vision sensor with inertial measurement unit for electronically stabilized event-based vision](http://doi.org/10.1109/ISCAS.2014.6865714)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2014. [YouTube](https://youtu.be/Tzy4WF6Qp-Y), [YouTube 2: Stabilizing DVS output with IMU rate gyros](https://youtu.be/_twn_x94ins), [YouTube 3: hallway scene](https://youtu.be/xRfAab89cH0)
- <a name="Rodriguez22icraw"></a>Rodriguez-Gomez, J.P., Gallego, G., Martinez-de Dios, J.R., Ollero, A.,  
*[Stabilizing Event Data on Flapping-wing Robots for Simpler Perception](https://zenodo.org/record/6602940/files/Workshop_Flapping_Wing_Challenges.pdf)*,  
IEEE Int. Conf. Robotics and Automation (ICRA) Workshop on Challenges of Flapping-wing Aerial Robots, 2022. [Slides](https://grvc.us.es/newweb/wp-content/uploads/2022/06/icra2022_flappingwingworkshop.pdf)
- <a name="Rodriguez24ral"></a>Rodrıguez-Gomez, J.P., Martınez-de Dios, J.R., Ollero, A., Gallego, G.,  
*[On the Benefits of Visual Stabilization for Frame- and Event-based Perception](https://doi.org/10.1109/LRA.2024.3450290)*,  
IEEE Robotics and Automation Letters (RA-L) 2024. [PDF](https://arxiv.org/pdf/2408.15602), [YouTube](https://www.youtube.com/watch?v=LAOztE-8FfY), [Code](https://github.com/tub-rip/visual_stabilization).

<a name="polarization-reconstruction"></a>
### Polarization Reconstruction
- <a name="Mei23cvpr"></a>Mei, H., Wang, Z., Yang, X., Wei, X., Delbruck, T.,  
*[Deep Polarization Reconstruction with PDAVIS Events](https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [PDF](https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.pdf), [code](https://github.com/SensorsINI/e2p).

<a name="depth-estimation"></a>
## Depth Estimation (3D Reconstruction)

<a name="depth-mono"></a>
### Monocular Depth Estimation
- <a name="Rebecq17ijcv"></a>Rebecq, H., Gallego, G., Mueggler, E., Scaramuzza, D.,  
*[EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time](https://doi.org/10.1007/s11263-017-1050-6)*,  
Int. J. of Computer Vision (IJCV), 126(12):1394-1414, 2018. [PDF](http://rpg.ifi.uzh.ch/docs/IJCV17_Rebecq.pdf), [YouTube](https://youtu.be/EFpZcpd9XJ0), [Code](https://github.com/uzh-rpg/rpg_emvs).
    - <a name="Rebecq16bmvc"></a>Rebecq, H., Gallego, G., Scaramuzza, D.,  
*[EMVS: Event-based Multi-View Stereo](http://www.bmva.org/bmvc/2016/papers/paper063/)*,  
British Machine Vision Conf. (BMVC), 2016. [PDF](http://rpg.ifi.uzh.ch/docs/BMVC16_Rebecq.pdf), [YouTube](https://www.youtube.com/watch?v=EUX3Tfx0KKE), [3D Reconstruction Experiments from a Train using an Event Camera](https://www.youtube.com/watch?v=fA4MiSzYHWA), [Code](https://github.com/uzh-rpg/rpg_emvs).
- [Kim et al., ECCV 2016](#Kim16eccv),  
*Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera*.
- <a name="Gallego18cvpr"></a>Gallego, G., Rebecq, H., Scaramuzza, D.,  
*[A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation](https://arxiv.org/pdf/1804.01306)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018. [PDF](http://rpg.ifi.uzh.ch/docs/CVPR18_Gallego.pdf),  [Poster](http://rpg.ifi.uzh.ch/docs/CVPR18_Gallego_poster.pdf),  [YouTube](https://youtu.be/KFMZFhi-9Aw),  [Spotlight presentation](https://youtu.be/IOntXI5iRzA).
- <a name="Haessig19srep"></a>Haessig, G., Berthelon, X., Ieng, S.-H., Benosman, R.,  
[A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision](https://doi.org/10.1038/s41598-019-40064-0),  
Scientific Reports 9, Article number: 3744 (2019). [PDF](https://www.neuromorphic-vision.com/public/publications/55/publication.pdf)
- <a name="Gallego19cvpr"></a>Gallego, G., Gehrig, M., Scaramuzza, D.,  
*[Focus Is All You Need: Loss Functions For Event-based Vision](http://rpg.ifi.uzh.ch/docs/CVPR19_Gallego.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF arXiv](https://arxiv.org/pdf/1904.07235), [Poster](http://rpg.ifi.uzh.ch/docs/CVPR19_Gallego_poster.pdf), [YouTube](https://youtu.be/SU_Fp4xS_g4?t=1059)
- <a name="Chaney19cvprw"></a>Chaney, K., Zhu, A., Daniilidis, K.,  
*[Learning Event-based Height from Plane and Parallax](https://doi.org/10.1109/IROS40897.2019.8968223)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [PDF](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Chaney_Learning_Event-Based_Height_From_Plane_and_Parallax_CVPRW_2019_paper.pdf), [Video pitch](https://youtu.be/S1OLnbnB_qo),  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2019.
- [Zhu et al., CVPR 2019](#Zhu19cvpr),  
*Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion.*
- <a name="Hidalgo20threedv"></a>Hidalgo-Carrió J., Gehrig D., Scaramuzza, D.,  
*[Learning Monocular Dense Depth from Events](https://arxiv.org/pdf/2010.08350.pdf)*,  
IEEE Int. Conf. 3D Vision (3DV), 2020. [PDF](http://rpg.ifi.uzh.ch/docs/3DV20_Hidalgo.pdf), [YouTube](https://youtu.be/Ne1KyyXd3_A), [Code](https://github.com/uzh-rpg/rpg_e2depth), [Project Page](http://rpg.ifi.uzh.ch/e2depth).
- <a name="Baudron20arxiv"></a>Baudron, A., Wang, Z. W., Cossairt, O., Katsaggelos, A. K.,  
*[E3D: Event-Based 3D Shape Reconstruction](https://arxiv.org/pdf/2012.05214)*,  
arXiv, 2020. [Code](https://github.com/alexisbdr/e3d).
- <a name="Gehrig21ral"></a>Gehrig, D., Rüegg, M., Gehrig, M., Hidalgo-Carrió J., Scaramuzza, D.,   
*[Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://doi.org/10.1109/LRA.2021.3060707)*,  
IEEE Robotics and Automation Letters (RA-L), 2021. [PDF](http://rpg.ifi.uzh.ch/docs/RAL21_Gehrig.pdf), [Code](http://rpg.ifi.uzh.ch/rpg_ramnet), [Project Page](http://rpg.ifi.uzh.ch/RAMNet.html).
- <a name="Muglikar23cvpr"></a>Muglikar, M., Bauersfeld, L., Moeys, D., Scaramuzza, D.,  
*[Event-based Shape from Polarization](https://arxiv.org/abs/2301.06855)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR23_Muglikar.pdf), [YouTube](https://youtu.be/sF3Ue2Zkpec), [code](https://github.com/uzh-rpg/ESfP).  
- <a name="Brebion23scia"></a>Brebion, V., Moreau, J., Davoine, F.,  
*[Learning to Estimate Two Dense Depths from LiDAR and Event Data](https://doi.org/10.1007/978-3-031-31438-4_34)*,  
22nd Scandinavian Conference on Image Analysis (SCIA 2023), 2023. [PDF](https://arxiv.org/pdf/2302.14444.pdf), [Project Page (suppl. mat., poster, code, dataset, videos)](https://vbrebion.github.io/ALED).
- <a name="Klenk23ral"></a>Klenk, S., Koestler, L., Scaramuzza, D., Cremers, D.,  
*[E-NeRF: Neural Radiance Fields from a Moving Event Camera](https://doi.org/10.1109/LRA.2023.3240646)*,  
IEEE Robotics and Automation Letters (RA-L) 8(3):1587-1594, 2023. [PDF](https://arxiv.org/pdf/2208.11300.pdf), [Code](https://github.com/knelk/enerf)

<a name="depth-mono-active"></a>
### Monocular Depth Estimation using Structured Light
- <a name="Brandli14fnins"></a>Brandli, C., Mantel, T.A., Hutter, M., Hoepflinger, M.A., Berner, R., Siegwart, R., Delbruck, T.,  
*[Adaptive Pulsed Laser Line Extraction for Terrain Reconstruction using a Dynamic Vision Sensor](https://doi.org/10.3389/fnins.2013.00275)*,  
Front. Neurosci. (2014), 7:275. [PDF](http://www.zora.uzh.ch/107736/1/fnins-07-00275.pdf), [YouTube](https://youtu.be/20OGD5Wwe9Q)
- <a name="Matsuda15iccp"></a>Matsuda, N., Cossairt, O., Gupta, M.,  
*[MC3D: Motion Contrast 3D Scanning](https://doi.org/10.1109/ICCPHOT.2015.7168370)*,  
IEEE Conf. Computational Photography (ICCP), 2015. [PDF](http://compphotolab.northwestern.edu/wordpress/wp-content/uploads/2015/04/dvs_031.pdf), [YouTube](https://youtu.be/m7qOEsTyVwU), [Project page](http://compphotolab.northwestern.edu/project/mc3d-motion-contrast-3d-laser-scanner/)
- <a name="Leroux18arxiv"></a>Leroux, T., Ieng, S.-H., Benosman, R.,  
*[Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns](https://arxiv.org/pdf/1811.10771)*,  
arXiv:1811.10771, 2018.
- <a name="mangalore2020neuromorphic"></a>Mangalore, A. R., Seelamantula, C. S., Thakur, C. S.,  
*[Neuromorphic Fringe Projection Profilometry](https://doi.org/10.1109/LSP.2020.3016251)*,  
IEEE Signal Process. Lett. (SPL), 27:1510-1514, 2020. [Project page](https://github.com/ashishrao7/NFPP)
- [Wang et al. JSEN](#Wang20jsen),  
*Temporal Matrices Mapping Based Calibration Method for Event-Driven Structured Light Systems.*
- <a name="Takatani21cvpr"></a>Takatani, T., Ito, Y., Ebisu, A., Zheng, Y., Aoto, T.,  
*[Event-Based Bispectral Photometry Using Temporally Modulated Illumination](https://openaccess.thecvf.com/content/CVPR2021/papers/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Project page](https://cigl.iit.tsukuba.ac.jp/portfolio/event-based-bispectral-photometry), [Suppl. Mat.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Takatani_Event-Based_Bispectral_Photometry_CVPR_2021_supplemental.pdf), [YouTube](https://youtu.be/BJLDMUQeDB8).
- <a name="Xueyan21oe"></a>Huang, X., Zhang, Y., Xiong Z.,  
*[High-speed structured light based 3D scanning using an event camera](https://doi.org/10.1364/OE.437944)*,  
Optics Express, 2021. [Video](https://osapublishing.figshare.com/articles/media/Tearing_A_Piece_Of_Paper_120Fps_mp4/14984802).
- <a name="Muglikar21esl"></a>Muglikar, M., Gallego, G., Scaramuzza, D.,  
*[ESL: Event-based Structured Light](http://rpg.ifi.uzh.ch/docs/3DV21_Muglikar_ESL.pdf)*,  
IEEE Int. Conf. 3D Vision (3DV), 2021. [Poster](http://rpg.ifi.uzh.ch/docs/3DV21_Muglikar_ESL_poster.pdf), [YouTube](https://youtu.be/SITmvZ8VLfs), [Project page and Dataset](http://rpg.ifi.uzh.ch/esl.html), [Code](https://github.com/uzh-rpg/ESL).
- <a name="Muglikar21threedv"></a>Muglikar, M., Moeys, D., Scaramuzza, D.,  
*[Event Guided Depth Sensing](https://rpg.ifi.uzh.ch/docs/3DV21_Muglikar.pdf)*,  
IEEE Int. Conf. 3D Vision (3DV), 2021. [YouTube](https://youtu.be/Rvv9IQLYjCQ).
- <a name="Wang22mfi"></a>Wang, H., Liu, T., He, C., Li, C., Liu, J., Yu, L.,  
*[Enhancing Event-based Structured Light Imaging with a Single Frame](https://doi.org/10.1109/MFI55806.2022.9913845)*,  
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2022.
- <a name="Morgenstern23cvprw"></a>Morgenstern, W., Gard, N., Baumann, S., Hilsmann, A., Eisert, P.,  
*[X-maps: Direct Depth Lookup for Event-based Structured Light Systems](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Morgenstern_X-Maps_Direct_Depth_Lookup_for_Event-Based_Structured_Light_Systems_CVPRW_2023_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2023. [Project page](https://fraunhoferhhi.github.io/X-maps/), [Code](https://github.com/fraunhoferhhi/X-maps).  

<a name="object-mono"></a>
### Monocular Object Reconstruction
- <a name="Wang22eccv"></a>Wang, Z., Chaney, K., Daniilidis, K.,  
*[EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670278.pdf)*,  
European Conference on Computer Vision (ECCV), 2022. [PDF](https://arxiv.org/pdf/2304.05296.pdf), [Project Page](https://www.cis.upenn.edu/~ziyunw/evac3d/).
- <a name="Chen23icvr"></a>Chen, H., Chung, V., Tan, L., Chen, X.,  
*[Dense Voxel 3D Reconstruction Using a Monocular Event Camera](https://doi.org/10.1109/ICVR57957.2023.10169359)*,  
Int. Conf. Virtual Reality (ICVR), 2023. [PDF](https://arxiv.org/pdf/2309.00385), [Dataset](https://www.kaggle.com/datasets/hche8927/synthevox3d).
- <a name="Qi23iccv"></a>Qi, Y., Zhu, L., Zhang, Y., Li, J.,  
*[E<sup>2</sup>NeRF: Event Enhanced Neural Radiance Fields from Blurry Images](https://doi.org/10.1109/ICCV51070.2023.01219)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2023. [PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376624), [Project Page](https://icvteam.github.io/E2NeRF.html), [Code](https://github.com/iCVTEAM/E2NeRF). [Dataset](https://drive.google.com/drive/folders/1XhOEp4UdLL7EnDNyWdxxX8aRvzF53fWo).

<a name="depth-stereo"></a>
### Stereo Depth Estimation
- [Misha Mahowald’s Stereo Chip - Tobi Delbruck- 2020 Telluride Neuromorphic workshop](https://youtu.be/9mYTbZM9odc),  
A tour through Misha Mahowald's 1992 stereo fusion work at Caltech in Carver Mead's Physics of Computation lab.  
[Mahowald's PhD thesis, 1992](#Mahowald92PhD), *VLSI Analogs of Neuronal Visual Processing: A Synthesis of Form and Function*.
- <a name="Schraml07visapp"></a>Schraml, C., Schon, P., Milosevic, N.,  
*[Smartcam for real-time stereo vision - address-event based embedded system](http://doi.org/10.5220/0002057604660471)*,  
Int. Conf. Computer Vision Theory and Applications (VISAPP), 2007, pp. 466-471.
    - <a name="Schraml10iscas"></a>Schraml, S., Belbachir, A. N., Milosevic, N., Schon, P.,  
*[Dynamic stereo vision system for real-time tracking](https://doi.org/10.1109/ISCAS.2010.5537289)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2010.
    - <a name="Schraml10cvprw"></a>Schraml, S., Belbachir, A. N.,  
*[A spatio-temporal clustering method using real-time motion analysis on event-based 3D vision](https://doi.org/10.1109/CVPRW.2010.5543810)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2010. [PDF](http://www.belbachir.info/PDF/cvpr1_2010.pdf)
    - <a name="Schraml10cvprw2"></a>Schraml, S., Belbachir, A. N., Braendle, N.,  
*[A Real-time Pedestrian Classification Method for Event-based Dynamic Stereo Vision](https://doi.org/10.1109/CVPRW.2010.5543775)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2010. [PDF](http://www.belbachir.info/PDF/cvpr2_2010.pdf)
    - <a name="Schraml10cvprw3"></a>Schraml, S., Belbachir, A. N., Braendle, N.,  
*[Real-time classification of pedestrians and cyclists for intelligent counting of non-motorized traffic](https://doi.org/10.1109/CVPRW.2010.5543170)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2010. [PDF](http://www.belbachir.info/PDF/cvpr3_2010.pdf)
- <a name="Kogler09icvs"></a>Kogler, J., Sulzbachner, C., Kubinger, W.,  
*[Bio-inspired stereo vision system with silicon retina imagers](https://doi.org/10.1007/978-3-642-04667-4_18)*,  
Int. Conf. Computer Vision Systems (ICVS), 2009.
    - <a name="Kogler11icvs"></a>Kogler, J., Humenberger, M., Sulzbachner, C.,  
*[Event-Based Stereo Matching Approaches for Frameless Address Event Stereo Data](http://doi.org/10.1007/978-3-642-24028-7_62)*,  
Int. Symp. Visual Computing (ISVC) 2011, Advances in Visual Computing, pp. 674-685.
    - <a name="Kogler11atasc"></a>Kogler, J., Sulzbachner, C., Humenberger, M., Eibensteiner, F.,  
*[Address-Event Based Stereo Vision with Bio-Inspired Silicon Retina Imagers](http://doi.org/10.5772/12941)*,  
Advances in Theory and Applications of Stereo Vision (2011), pp. 165-188.
    - [Kogler, J., Ph.D. Thesis 2016](#Kogler16PhD),  
*Design and evaluation of stereo matching techniques for silicon retina cameras*.
- <a name="Kogler10scce"></a>Kogler, J., Sulzbachner, C., Eibensteiner, F., Humenberger, M.,  
*[Address-Event Matching for a Silicon Retina based Stereo Vision System](https://pdfs.semanticscholar.org/2a76/ebed789fdca9dc9e40dc412f6bba3cfa3ef2.pdf)*,  
Int. Conf. from Scientific Computing to Computational Engineering (IC-SCCE), 2010.
    - <a name="Sulzbachner10elmar"></a>Sulzbachner, C., Kogler, J., Eibensteiner, F.,  
*[A novel verification approach for silicon retina stereo matching](https://ieeexplore.ieee.org/abstract/document/5606120)*,  
IEEE Int. Symp. Electronics in Marine (ELMAR), 2010.
    - <a name="Sulzbachner11cvprw"></a>Sulzbachner, C., Zinner, C., Kogler, J.,  
*[An optimized silicon retina stereo matching algorithm using time-space correlation](https://doi.org/10.1109/CVPRW.2011.5981722)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011. [PDF](http://encevis.de/documents/Paper/2011_06_20-25.pdf)
    - <a name="Eibensteiner11eurocast"></a>Eibensteiner, F., Kogler, J., Sulzbachner, C., Scharinger, J.,  
*[Stereo-Vision Algorithm Based on Bio-Inspired Silicon Retinas for Implementation in Hardware](https://doi.org/10.1007/978-3-642-27549-4_80)*,  
Int. Conf. Computer Aided Systems Theory EUROCAST, LNCS, pp. 624–631, 2011.
    - <a name="Eibensteiner14cvprw"></a>Eibensteiner, F., Kogler, J., Scharinger, J.,  
*[A High-Performance Hardware Architecture for a Frameless Stereo Vision Algorithm Implemented on a FPGA Platform](https://doi.org/10.1109/CVPRW.2014.97)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2014. [PDF](http://openaccess.thecvf.com/content_cvpr_workshops_2014/W17/papers/Eibensteiner_A_High-Performance_Hardware_2014_CVPR_paper.pdf)
    - <a name="Eibensteiner17radio"></a>Eibensteiner, F., Brachtendorf, H. G., Scharinger, J.,  
*[Event-driven stereo vision algorithm based on silicon retina sensors](http://doi.org/10.1109/RADIOELEK.2017.7937602)*,  
27th Int. Conf. Radioelektronika, 2017.
- <a name="Belbachir11cvprw"></a>Belbachir, A.N., Schraml, S., Nowakoska, A.,  
*[Event-Driven Stereo Vision for Fall Detection](https://doi.org/10.1109/CVPRW.2011.5981819)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011.
    - <a name="Belbachir11iccvw"></a>Belbachir, A.N., Nowakoska, A., Schraml, S., Wiesmann, G., Sablatnig, R.,  
*[Event-Driven Feature Analysis in a 4D Spatiotemporal Representation](https://doi.org/10.1109/ICCVW.2011.6130437)*,  
IEEE Int. Conf. Computer Vision Workshops (ICCVW), 2011.
    - <a name="Belbachir12iscas"></a>Belbachir, A.N., Litzenberger, M., Schraml, S., Hofstätter, M., Bauer, D., Schön, P., Humenberger, M., Sulzbachner, C., Lunden, T., Merne, M.,  
*[CARE: A dynamic stereo vision sensor system for fall detection](https://doi.org/10.1109/ISCAS.2012.6272141)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2012.
    - <a name="Humenberger12cvprw"></a>Humenberger, M., Schraml, S., Sulzbachner, C., Belbachir, A.N., Srp A., Vajda, F.,  
*[Embedded Fall Detection with a Neural Network and Bio-inspired Stereo Vision](https://doi.org/10.1109/CVPRW.2012.6238896)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
- <a name="Benosman11tnn"></a>Benosman, R., Ieng, S. H., Rogister, P., Posch, C.,  
*[Asynchronous Event-Based Hebbian Epipolar Geometry](https://doi.org/10.1109/TNN.2011.2167239)*,  
IEEE Trans. Neural Netw., 22(11):1723-1734, 2011. [PDF](https://www.neuromorphic-vision.com/public/publications/10/publication.pdf)
- <a name="Rogister12tnnls"></a>Rogister, P. , Benosman, R., Ieng, S.-H., Lichtsteiner, P., Delbruck, T.,  
*[Asynchronous Event-Based Binocular Stereo Matching](https://doi.org/10.1109/TNNLS.2011.2180025)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 23(2):347-353, 2012. [PDF](https://www.neuromorphic-vision.com/public/publications/16/publication.pdf)
- <a name="Carneiro13neunet"></a>Carneiro, J., Ieng, S.-H., Posch, C., Benosman, R.,  
*[Event-based 3D reconstruction from neuromorphic retinas](https://doi.org/10.1016/j.neunet.2013.03.006)*,  
Neural Networks (2013), 45:27-38. [PDF](https://www.neuromorphic-vision.com/public/publications/5/publication.pdf), [YouTube](https://youtu.be/XLMhTbNoHR0), [YouTube](https://youtu.be/XLMhTbNoHR0), [YouTube](https://youtu.be/KqIRza6dB4E), [YouTube](https://youtu.be/WKLWCaOxhp8), [YouTube](https://youtu.be/5DK6TlMyDxQ), [YouTube](https://youtu.be/neKsSMsaYNI)
    - [Carneiro, Ph.D. Thesis, 2014](#Carneiro14PhD),  
*Asynchronous Event-Based 3D Vision*.
- [Lee et al., TNNLS 2014](#Lee14tnnls)
- <a name="Piatkowska14mst"></a>Piatkowska, E., Belbachir, A. N., Gelautz, M.,  
*[Cooperative and asynchronous stereo vision for dynamic vision sensors](http://dx.doi.org/10.1088/0957-0233/25/5/055108)*,  
Meas. Sci. Technol. (2014), 25(5).
    - <a name="Piatkowska13iccvw"></a>Piatkowska, E., Belbachir, A. N., Gelautz, M.,  
*[Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor Using an Adaptive Cooperative Approach](https://doi.org/10.1109/ICCVW.2013.13)*,  
IEEE Int. Conf. Computer Vision Workshops (ICCVW), 2013.
    - <a name="Piatkowska17cvprw"></a>Piatkowska, E., Kogler, J., Belbachir, A. N., Gelautz, M.,  
*[Improved Cooperative Stereo Matching for Dynamic Vision Sensors with Ground Truth Evaluation](http://doi.org/10.1109/CVPRW.2017.51)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 370-377. [PDF](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w4/papers/Piatkowska_Improved_Cooperative_Stereo_CVPR_2017_paper.pdf).
- <a name="Camunas14fnins"></a>Camuñas-Mesa, L. A., Serrano-Gotarredona, T., Ieng, S. H., Benosman, R. B., Linares-Barranco, B.,  
*[On the use of orientation filters for 3D reconstruction in event–driven stereo vision](https://doi.org/10.3389/fnins.2014.00048)*,  
Front. Neurosci. (2014), 8:48. [PDF](https://www.neuromorphic-vision.com/public/publications/29/publication.pdf)
    - <a name="Camunas14iscas"></a>Camuñas-Mesa, L. A., Serrano-Gotarredona, T., Linares-Barranco, B., Ieng, S., Benosman, R.,  
*[Event-Driven Stereo Vision with Orientation Filters](https://doi.org/10.1109/ISCAS.2014.6865114)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2014.
- <a name="Kogler14jei"></a>Kogler, J., Eibensteiner, F., Humenberger, M., Sulzbachner, C., Gelautz, M., Scharinger, J.,  
*[Enhancement of sparse silicon retina-based stereo matching using belief propagation and two-stage postfiltering](https://doi.org/10.1117/1.JEI.23.4.043011)*,  
J. Electronic Imaging, 23(4), 043011 (2014).
    - [Kogler, J., Ph.D. Thesis 2016](#Kogler16PhD),  
*Design and evaluation of stereo matching techniques for silicon retina cameras*.
- <a name="Firouzi16npl"></a>Firouzi, M. and Conradt, J.,  
*[Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas](http://doi.org/10.1007/s11063-015-9434-5),*  
Neural Processing Letters, 43(2):311-326, Apr. 2016. [PDF](https://mediatum.ub.tum.de/doc/1254531/131347.pdf)
    - <a name="Dikov17lmlncs"></a>Dikov, G., Firouzi, M., Röhrbein, F., Conradt, J., Richter, C.,  
*[Spiking Cooperative Stereo-Matching at 2 ms Latency with Neuromorphic Hardware](https://doi.org/10.1007/978-3-319-63537-8_11)*,  
Conf. Biomimetic and Biohybrid Systems. Living Machines 2017: Biomimetic and Biohybrid Systems, pp. 119-137. LNCS, vol 10384. Springer, Cham.  [PDF](https://www.researchgate.net/publication/318449954_Spiking_Cooperative_Stereo-Matching_at_2_ms_Latency_with_Neuromorphic_Hardware),  [Videos](https://figshare.com/s/0d9fb146149b832ed8ec)
    - <a name="Kaiser18icann"></a>Kaiser, J., Weinland, J., Keller, P., Steffen, L., Vasquez Tieck, J.C., Reichard, D., Roennau, A., Conradt, J., Dillmann, R.,  
*[Microsaccades for Neuromorphic Stereo Vision](https://doi.org/10.1007/978-3-030-01418-6_24)*,  
Int. Conf. Artificial Neural Networks (ICANN), 2018.
- <a name="Zou16icip"></a>Zou, D., Guo, P., Wang, Q., Wang, X., Shao, G., Shi, F., Li, J., Park, P.K.J.,  
*[Context-Aware Event-driven Stereo Matching](https://doi.org/10.1109/ICIP.2016.7532523)*,  
IEEE Int. Conf. Image Processing (ICIP), 2016.
- <a name="Osswald17srep"></a>Osswald, M., Ieng, S.-H., Benosman, R., Indiveri, G.,  
*[A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems](http://doi.org/10.1038/srep40703)*,  
Scientific Reports 7, Article number: 40703 (2017). [PDF](https://www.neuromorphic-vision.com/public/publications/44/publication.pdf)
    - [Haessig et al., AICAS 2019](#Haessig19aicas),  
*Neuromorphic networks on the SpiNNaker platform*.
- <a name="Zou17bmvc"></a>Zou, D., Shi, F., Liu, W., Li, J., Wang, Q., Park, P.K.J., Shi, C.-W., Roh, Y.J., Ryu, H.,  
*[Robust Dense Depth Map Estimation from Sparse DVS Stereos](https://www.dropbox.com/s/ee6dn8zy4odpfwl/0096.pdf?dl=1)*,  
British Machine Vision Conf. (BMVC), 2017. [Supp. Material](https://www.dropbox.com/s/eztqo109iue6ned/Active%20Papers%20Paper%2096%20%20Supplementary%20File.zip?dl=1).
- Camuñas-Mesa, L. A., Serrano-Gotarredona, T., Ieng, S., Benosman, R., Linares-Barranco, B.,  
*[Event-driven Stereo Visual Tracking Algorithm to Solve Object Occlusion](https://doi.org/10.1109/TNNLS.2017.2759326)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 2017.
- <a name="Xie17fnins"></a>Xie, Z., Chen, S., Orchard, G.  
*[Event-Based Stereo Depth Estimation Using Belief Propagation](https://doi.org/10.3389/fnins.2017.00535)*,  
Front. Neurosci. (2017), 11:535.  [YouTube](https://youtu.be/ngJpY1lcbdw)
- [Everding, L., Ph.D. Thesis 2018](#Everding18PhD),  
*Event-Based Depth Reconstruction Using Stereo Dynamic Vision Sensors*.  
    - [Kaelber, F., Bachelor Thesis 2016](#Kaelber16BS),  
*A probabilistic method for event stream registration*.
    - [Galanis, M., Bachelor Thesis 2016](#Galanis16BS),  
*DVS event stream registration*.
- <a name="Andreopoulos18cvpr"></a>Andreopoulos, A., Kashyap, H.J., Nayak, T.K., Amir, A., Flickner, M.D.,  
*[A Low Power, High Throughput, Fully Event-Based Stereo System](http://openaccess.thecvf.com/content_cvpr_2018/papers/Andreopoulos_A_Low_Power_CVPR_2018_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018.
    - [Stereo Dataset](https://ibm.ent.box.com/s/3hiq58ww1pbbjrinh367ykfdf60xsfm8).
- <a name="Ieng18fnins"></a>Ieng, S.-H., Carneiro, J., Osswald, M., Benosman, R.,  
*[Neuromorphic Event-Based Generalized Time-Based Stereovision](https://doi.org/10.3389/fnins.2018.00442)*,  
Front. Neurosci. (2018), 12:442.
    - [Carneiro, Ph.D. Thesis, 2014](#Carneiro14PhD),  
*Asynchronous Event-Based 3D Vision - Chapter 4*.
- <a name="Zhu18eccv"></a>Zhu, A., Chen, Y., Daniilidis, K.,  
*[Realtime Time Synchronized Event-based Stereo](https://arxiv.org/abs/1803.09025)*,  
European Conf. Computer Vision (ECCV), 2018. [YouTube](https://youtu.be/4oa7e4hsrYo)
- <a name="Zhou18eccv"></a>Zhou, Y., Gallego, G., Rebecq, H., Kneip, L., Li, H., Scaramuzza, D.,  
*[Semi-Dense 3D Reconstruction with a Stereo Event Camera](http://rpg.ifi.uzh.ch/docs/ECCV18_Zhou.pdf)*,  
European Conf. Computer Vision (ECCV), 2018. [Poster](http://rpg.ifi.uzh.ch/docs/ECCV18_Zhou_poster.pdf),  [YouTube](https://youtu.be/Qrnpj2FD1e4).
    - [Zhou et al., TRO 2021](#Zhou21tro),  
*ESVO: Event-based Stereo Visual Odometry*.
- <a name="DominguezMorales19electr"></a>Dominguez-Morales, M., Dominguez-Morales, J. P., Jimenez-Fernandez, A., Linares-Barranco, A., Jimenez-Moreno, G.,  
*[Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)](https://doi.org/10.3390/electronics8040410)*,  
Electronics 2019, 8(4), 410.
- <a name="Steffen19fnbot"></a>Steffen, L., Reichard, D., Weinland, J., Kaiser, J., Roennau, A., Dillmann, R.,  
*[Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms](https://doi.org/10.3389/fnbot.2019.00028)*,  
Front. Neurorobot. (2019) 13:28.
- <a name="Steffen19case"></a>Steffen, L., Hauck, B., Kaiser, J., Weinland, J., Ulbrich, S., Reichard, D., Roennau, A., Dillmann, R.,  
*[Creating an Obstacle Memory Through Event-Based Stereo Vision and Robotic Proprioception](https://doi.org/10.1109/COASE.2019.8843238)*,  
IEEE Int. Conf. Automation Science and Engineering (CASE), 2019.
- <a name="Hadviger19ecmr"></a>Hadviger, A., Markovic, I., Petrovic, I.,  
*[Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors](https://arxiv.org/pdf/1907.07518)*,  
European Conf. Mobile Robots (ECMR), 2019. [PDF arXiv](https://arxiv.org/pdf/1907.07518).
- <a name="Tulyakov19iccv"></a>Tulyakov, S., Fleuret, F., Kiefel, M., Gehler, P., Hirsch., M.,  
*[Learning an event sequence embedding for dense event-based deep stereo](https://openaccess.thecvf.com/content_ICCV_2019/html/Tulyakov_Learning_an_Event_Sequence_Embedding_for_Dense_Event-Based_Deep_Stereo_ICCV_2019_paper.html)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2019. [PDF](https://fleuret.org/papers/tulyakov-et-al-iccv2019.pdf), [Video](https://youtu.be/my3jocjpD0U?t=1447)
- <a name="Steffen19icar"></a>Steffen, L., Ulbrich, S., Roennau, A., Dillmann, R.,  
*[Multi-View 3D Reconstruction With Self-Organizing Maps on Event-Based Data](https://doi.org/10.1109/ICAR46387.2019.8981569)*,  
IEEE Int. Conf. Advanced Robotics (ICAR), 2019.
- <a name="Hadviger20advrob"></a>Hadviger, A., Marković, I., Petrović, I.,  
*[Stereo Dense Depth Tracking Based on Optical Flow using Frames and Events](https://lamor.fer.hr/images/50036607/hadviger-disparity-advrob.pdf)*,  
Advanced Robotics, 2020.
- <a name="soikat21aaai"></a>Ahmed, S. H., Jang, H. W., Uddin, S. M. N., & Jung, Y. J.,  
*[Deep Event Stereo Leveraged by Event-to-Image Translation](https://ojs.aaai.org/index.php/AAAI/article/view/16171)*,  
AAAI Conf. Artificial Intelligence, 2021.
[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/16171/15978), [Video](https://youtu.be/ow2TsNMo_T4), [Project page](https://sites.google.com/site/gachoncvip/projects/deep-event-based-stereo-matching)
- <a name="Wang21iros"></a>Wang, Z., Pan, L., Ng, Y., Zhuang, Z., Mahony, R.,   
*[Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://doi.org/10.1109/IROS51168.2021.9636312)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021.
[PDF](https://arxiv.org/pdf/2110.04988.pdf), [Video](https://www.youtube.com/watch?v=Azu7rJSPGNc), [Code and Data](https://github.com/ziweiWWANG/SHEF).
- <a name="Ghosh22aisy"></a>Ghosh, S., Gallego, G.,   
*[Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion (MCEMVS)](https://arxiv.org/pdf/2207.10494)*,  
Advanced Intelligent Systems (AISY), 2022.
[YouTube](https://youtu.be/o7Bxg9XlHmg), [Presentation at IEEE MFI workshop 2022 (YouTube)](https://youtu.be/MaRoJ16obdI), [Slides MFI 2022](https://drive.google.com/file/d/12bFp1sW5vRIUhnRLNk-g8BCe7rlH8x94/view), [Presentation at the GRASP Laboratory (UPenn) seminar](https://youtu.be/8v-bYCVL9hw), [Project page (with Code)](https://github.com/tub-rip/dvs_mcemvs),
    - <a name="Ghosh22asn"></a>Ghosh, S., Gallego, G.,   
    *[Silicon retinas to help robots navigate the world](https://www.advancedsciencenews.com/silicon-retinas-to-help-robots-navigate-the-world/)*,  
    Advanced Science News, 2022.
    - <a name="Ghosh22eccvw"></a>Ghosh, S., Gallego, G.,   
    *[Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion](https://arxiv.org/pdf/2210.08927)*,  
    2nd Int. Ego4D Workshop at European Conf. Computer Vision Workshops (ECCVW), 2022.
- <a name="Uddin22tcsvt"></a>Uddin, S.M.N., Ahmed, S.H., Jung, Y.J.,  
*[Unsupervised Deep Event Stereo for Depth Estimation](https://doi.org/10.1109/TCSVT.2022.3189480)*,  
IEEE Trans. Circuits Syst. Video Technol. (TCSVT), 32(11):7489-7504, 2022.
- <a name="Gu23eistereo"></a>Gu, J., Zhou, J., Chu, R.S.W., Chen, Y., Zhang, J., Cheng, X., Zhang, S., Ren, J.S.,  
*[Self-Supervised Intensity-Event Stereo Matching](https://arxiv.org/abs/2211.00509)*,  
J. Imaging Sci. Technol. 2023. [Project page and Code](https://ringoswchu.com/Research/iestereo/).
- [Ghosh and Gallego, arxiv 2024](#Ghosh24stereo),  
*Event-based Stereo Depth Estimation: A Survey*.


<a name="depth-stereo-active"></a>
### Stereo Depth Estimation using Structured Light
- <a name="Martel18iscas"></a>Martel, J.N.P., Mueller, J., Conradt, J., Sandamirskaya, Y.,  
*[An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors](http://dx.doi.org/10.1109/ISCAS.2018.8351411)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2018.
    - <a name="Martel18iscas"></a>Martel, J.N.P., Müller, J., Conradt, J., Sandamirskaya, Y.,  
*[Live Demonstration: An Active System for Depth Reconstruction using Event-Based Sensors](https://doi.org/10.1109/ISCAS.2018.8351861)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2018.


<a name="depth-stereo-pano"></a>
### Stereoscopic Panoramic Imaging
- <a name="TUCO14ait"></a>[smart eye TUCO-3D camera](https://www.ait.ac.at/fileadmin/mc/digital_safety_security/downloads/Factsheet_-_Panoramic-Imaging-Sensor_en.pdf),  
Stereoscopic panoramic imaging camera based on dynamic vision sensors. [PDF](https://www.ait.ac.at/fileadmin/mc/digital_safety_security/downloads/TUCO-3D_Vision2014.pdf)
- <a name="Belbachir10icdsc"></a>Belbachir, A. N., Pflugfelder, R., Gmeiner, P.,  
*[A Neuromorphic Smart Camera for Real-time 360deg distortion-free Panoramas](https://doi.org/10.1145/1865987.1866022)*,  
IEEE Conference on Distributed Smart Cameras (ICDSC), 2010. [PDF](http://www.belbachir.info/PDF/icdsc2010.pdf)
- <a name="Belbachir12iscas2"></a>Belbachir, A.N., Mayerhofer, M., Matolin, D., Colineau, J.,  
*[Real-time 360 degrees Panoramic Views using BiCa360, the Fast Rotating Dynamic Vision Sensor to up to 10 Rotations per Sec](https://doi.org/10.1109/ISCAS.2012.6272139)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2012.
- <a name="Belbachir12icdsc"></a>Belbachir, A.N., Mayerhofer, M., Matolin, D., Colineau, J.,  
*[360SCAN: High-speed rotating line sensor for real-time 360 degrees panoramic vision](https://ieeexplore.ieee.org/document/6470127)*,  
IEEE Int. Conf. Distributed Smart Cameras (ICDSC), 2012.
- <a name="Belbachir14cvprw"></a>Belbachir, A. N., Schraml, S., Mayerhofer, M., Hofstatter, M.,  
*[A Novel HDR Depth Camera for Real-time 3D 360-degree Panoramic Vision](https://doi.org/10.1109/CVPRW.2014.69)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2014, pp. 419-426. [PDF](http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W13/papers/Belbachir_A_Novel_HDR_2014_CVPR_paper.pdf)
- <a name="Schraml15cvpr"></a>Schraml, S., Belbachir, A. N., Bischof, H.,  
*[Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision](https://doi.org/10.1109/CVPR.2015.7298644)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2015, pp. 466-474. [PDF](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schraml_Event-Driven_Stereo_Matching_2015_CVPR_paper.pdf). [Slides](https://www.anyline.io/wp-content/uploads/2016/03/event-driven-stereo-for-3d-360deg-panoramic-vision.pdf).
- <a name="Schraml16tie"></a>Schraml, S., Belbachir, A. N., Bischof, H.,  
*[An Event-Driven Stereo System for Real-Time 3-D 360° Panoramic Vision](https://doi.org/10.1109/TIE.2015.2477265)*,  
IEEE Trans. Ind. Electron., 63(1):418-428, 2016.


<a name="slam"></a>
## SLAM (Simultaneous Localization And Mapping)

<a name="slam-localization"></a>
### Localization, Ego-Motion Estimation
- <a name="Weikersdorfer12robio"></a>Weikersdorfer, D. and Conradt, J.,  
*[Event-based particle filtering for robot self-localization](http://doi.org/10.1109/ROBIO.2012.6491077)*,  
IEEE Int. Conf. Robotics and Biomimetics (ROBIO), 2012. [PDF](https://mediatum.ub.tum.de/doc/1215541/835468.pdf)
- <a name="Censi13iros"></a>Censi, A., Strubel, J., Brandli, C., Delbruck, T., Scaramuzza, D.,  
*[Low-latency localization by Active LED Markers tracking using a Dynamic Vision Sensor](https://doi.org/10.1109/IROS.2013.6696456)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2013. [PDF](http://rpg.ifi.uzh.ch/docs/IROS13_Censi.pdf), [Slides](http://rpg.ifi.uzh.ch/docs/IROS13_Censi.ppt)
- <a name="Mueggler14iros"></a>Mueggler, E., Huber, B., Scaramuzza, D.,  
*[Event-based, 6-DOF Pose Tracking for High-Speed Maneuvers](https://doi.org/10.1109/IROS.2014.6942940)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Chicago, IL, 2014, pp. 2761-2768. [PDF](http://rpg.ifi.uzh.ch/docs/IROS14_Mueggler.pdf), [YouTube](https://youtu.be/LauQ6LWTkxM)
- <a name="Gallego15arxiv"></a>Gallego, G., Forster, C., Mueggler, E., Scaramuzza, D.,  
*[Event-based Camera Pose Tracking using a Generative Event Model](https://arxiv.org/pdf/1510.01972)*,  
arXiv:1510.01972, 2015.
- <a name="Mueggler15rss"></a>Mueggler, E., Gallego G., Scaramuzza, D.,   
*[Continuous-Time Trajectory Estimation for Event-based Vision Sensors](http://dx.doi.org/10.15607/RSS.2015.XI.036)*,  
Robotics: Science and Systems (RSS), 2015. [PDF](http://rpg.ifi.uzh.ch/docs/RSS15_Mueggler.pdf), [PPT](http://rpg.ifi.uzh.ch/docs/RSS15_Mueggler.pptm),  [Poster](http://rpg.ifi.uzh.ch/docs/RSS15_Mueggler_poster.pdf)
- <a name="Reverter16fnins"></a>Reverter Valeiras, D., Orchard, G., Ieng, S.-H., Benosman, R.,  
*[Neuromorphic Event-Based 3D Pose Estimation](https://doi.org/10.3389/fnins.2015.00522)*.  
Front. Neurosci. (2016), 9:522. [PDF](https://www.neuromorphic-vision.com/public/publications/24/publication.pdf), [Suppl. Mat.](https://www.neuromorphic-vision.com/public/publications/24/supplementaryMaterial.zip), [YouTube](https://youtu.be/feBozLYZhB8)
- <a name="Reverter16fninsb"></a>Reverter Valeiras, D., Kime, S., Ieng, S.-H., Benosman, R.,  
*[An Event-Based Solution to the Perspective-n-Point Problem](https://doi.org/10.3389/fnins.2016.00208)*,  
Front. Neurosci. (2016), 10:208. [PDF](https://www.neuromorphic-vision.com/public/publications/25/publication.pdf), [Suppl. Mat.](https://www.neuromorphic-vision.com/public/publications/25/supplementaryMaterial.zip)
- <a name="Yuan16icra"></a>Yuan, W., Ramalingam, S.,  
*[Fast Localization and Tracking using Event Sensors](https://doi.org/10.1109/ICRA.2016.7487657)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2016. [PDF](http://people.csail.mit.edu/yuan_wz/OtherProj/Event_ICRA16.pdf), [Video](http://people.csail.mit.edu/yuan_wz/OtherProj/EventLoc_demo.mp4)
- [Mueggler et al., IJRR 2017](#Mueggler17ijrr).  
*The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.*
- <a name="Gallego18tpami"></a>Gallego, G., Lund, J.E.A., Mueggler, E., Rebecq, H., Delbruck, T., Scaramuzza, D.,  
*[Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](http://dx.doi.org/10.1109/TPAMI.2017.2769655)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2018. [PDF](http://rpg.ifi.uzh.ch/docs/PAMI17_Gallego.pdf),  [YouTube](https://youtu.be/iZZ77F-hwzs?t=5), [Datasets](http://rpg.ifi.uzh.ch/direct_event_camera_tracking/index.html)
- <a name="Nguyen19cvprw"></a>Nguyen, A., Do, T.-T., Caldwell, D. G., Tsagarakis, N. G.,  
*[Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Nguyen_Real-Time_6DOF_Pose_Relocalization_for_Event_Cameras_With_Stacked_Spatial_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [PDF](https://arxiv.org/abs/1708.09011). [Project page](https://github.com/nqanh/pose_relocalization). [Video pitch](https://youtu.be/Bwmmt7dqTIw)
- [Maqueda et al., CVPR 2018](#Maqueda18cvpr).  
*Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars.*
- [Gallego et al., CVPR 2018](#Gallego18cvpr),  
*A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation*.
- <a name="Bryner19icra"></a>Bryner, S., Gallego, G., Rebecq, H., Scaramuzza, D.,  
*[Event-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization](http://rpg.ifi.uzh.ch/docs/ICRA19_Bryner.pdf)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2019. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA19_Bryner.pdf), [YouTube](https://youtu.be/ISgXVgCR-lE), [Project page and Datasets](http://rpg.ifi.uzh.ch/direct_event_camera_tracking/index.html)
- [Gallego et al., CVPR 2019](#Gallego19cvpr),  
*Focus Is All You Need: Loss Functions For Event-based Vision*.
- [Zhu et al., CVPR 2019](#Zhu19cvpr),  
*Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion.*
- <a name="Xu20tci"></a>Xu, J., Jiang, M., Yu, L., Yang, W., Wang, W.,  
*[Robust Motion Compensation for Event Cameras With Smooth Constraint](http://dx.doi.org/10.1109/TCI.2020.2964255)*,  
IEEE Trans. Comput. Imag. (TCI), 6:604-614, 2020.
- <a name="Fischer20ral"></a>Fischer, T., Milford, M.,  
*[Event-based visual place recognition with ensembles of spatio-temporal windows](https://doi.org/10.1109/LRA.2020.3025505)*,  
IEEE Robotics and Automation Letters (RA-L), 5(4):6924-6931, 2020. [PDF including Suppl. Mat.](https://arxiv.org/abs/2006.02826), [Code](https://github.com/Tobias-Fischer/ensemble-event-vpr)
- <a name="Kreiser20fnins"></a>Kreiser, R., Renner, A., Leite, V.R.C., Serhan, B., Bartolozzi, C., Glover, A., Sandamirskaya, Y.,  
[An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot](https://doi.org/10.3389/fnins.2020.00551),  
Front. Neurosci. (2020), 14:551.
- <a name="Nunes20eccv"></a>Nunes, U.M., Demiris, Y.,  
*[Entropy Minimisation Framework for Event-based Vision Model Estimation](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500154.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500154-supp.pdf), [YouTube](https://youtu.be/3pyD9oCESa0), [Code](https://github.com/ImperialCollegeLondon/EventEMin)
    - <a name="Nunes21cvprw"></a>Nunes, U.M., Demiris, Y.,  
*[Live Demonstration: Incremental Motion Estimation for Event-Based Cameras by Dispersion Minimisation](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Nunes_Live_Demonstration_Incremental_Motion_Estimation_for_Event-Based_Cameras_by_Dispersion_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Code](https://github.com/ImperialCollegeLondon/event_emin_ros)
    - <a name="Nunes21tpami"></a>Nunes, U.M., Demiris, Y.,  
*[Robust Event-based Vision Model Estimation by Dispersion Minimisation](https://doi.org/10.1109/TPAMI.2021.3130049)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2021. [Suppl. Mat.](https://doi.org/10.1109/TPAMI.2021.3130049/mm1), [Code](https://github.com/ImperialCollegeLondon/EventEMin)
- <a name="Peng20eccv"></a>Peng, X., Wang, Y., Gao, L., Kneip, L.,  
*[Globally-Optimal Event Camera Motion Estimation](https://doi.org/10.1007/978-3-030-58574-7_4)*,  
European Conf. Computer Vision (ECCV), 2020. [PDF](https://arxiv.org/abs/2203.03914), [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710052-supp.pdf), [YouTube](https://youtu.be/Px1puptSX9Q)
    - <a name="Peng21tpami"></a>Peng, X., Gao, L., Wang, Y., Kneip, L.,  
*[Globally-Optimal Contrast Maximisation for Event Cameras](https://doi.org/10.1109/TPAMI.2021.3053243)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 44(7):3479-3495, 2022. [PDF](https://arxiv.org/abs/2206.05127)
- <a name="Bertrand20ebccsp"></a>Bertrand, J., Yigit, A., Durand, S.,  
*[Embedded Event-based Visual Odometry](https://doi.org/10.1109/EBCCSP51266.2020.9291346)*,  
IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2020. [Code](https://github.com/Blabla51/Embedded-Event-based-Visual-Odometry)  
- <a name="Chamorro20bmvc"></a>Chamorro, W., Andrade-Cetto, J., Solà, J.,  
*[High-Speed Event Camera Tracking](https://www.bmvc2020-conference.com/conference/papers/paper_0366.html)*,  
British Machine Vision Conf. (BMVC), 2020. [PDF](https://www.bmvc2020-conference.com/assets/papers/0366.pdf), [Suppl. Mat.](https://www.bmvc2020-conference.com/assets/supp/0366_supp.zip), [PDF](https://arxiv.org/pdf/2010.02771)
- <a name="Chen20jsen"></a>Chen, G., Chen, W., Yang, Q., Xu, Z., Yang, L., Conradt, J., Knoll, A.,  
*[A Novel Visible Light Positioning System With Event-Based Neuromorphic Vision Sensor](https://doi.org/10.1109/JSEN.2020.2990752)*,  
IEEE Sensors Journal, 20(17):10211-10219, 2020.
- <a name="Kong20arxiv"></a>Kong, D., Fang, Z., Li, H., Hou, K., Coleman, S., Kerr, D.,  
*[Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition](https://arxiv.org/pdf/2011.03290)*,  
arXiv, 2020.
- <a name="Jiao21cvprw"></a>Jiao, J., Huang, H., Li, L., He, Z., Zhu, Y., Liu, M.,  
*[Comparing Representations in Tracking for Event Camera-Based SLAM](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Jiao_Comparing_Representations_in_Tracking_for_Event_Camera-Based_SLAM_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [YouTube](https://youtu.be/O1ow7c_OlDQ), [Code](https://github.com/gogojjh/ESVO_extension).
- <a name="Gu21iccv"></a>Gu, C., Learned-Miller, E., Sheldon, D., Gallego, G., Bideau, P.,  
*[The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data](http://vis-www.cs.umass.edu/motionSegmentation/website_ICCV21/ICCV2021-paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [Project page](http://vis-www.cs.umass.edu/motionSegmentation/website_ICCV21/index.html), [Code](https://github.com/pbideau/Event-ST-PPP)
- <a name="Peng21bmvc"></a>Peng, X., Xu, W., Yang, J., Kneip, L.,  
*[Continuous Event-Line Constraint for Closed-Form Velocity Initialization](https://www.bmvc2021-virtualconference.com/assets/papers/0877.pdf)*,  
British Machine Vision Conf. (BMVC), 2021, [PDF](https://www.bmvc2021-virtualconference.com/assets/papers/0877.pdf), [Video](https://www.bmvc2021-virtualconference.com/conference/papers/paper_0877.html)
- <a name="lee2021eventvlad"></a>Lee, A. J., Kim, A.,  
*[EventVLAD: Visual Place Recognition with Reconstructed Edges from Event Cameras](https://ieeexplore.ieee.org/abstract/document/9635907)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021, [PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9635907)
- <a name="Shiba22aisy"></a>Shiba, S., Aoki, Y., Gallego, G.,  
*[A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework](https://onlinelibrary.wiley.com/doi/10.1002/aisy.202200251)*,  
Advanced Intelligent Systems, 2022.  [PDF](https://arxiv.org/pdf/2212.07350), [YouTube](https://youtu.be/IwqA57yaBTo), [Project page](https://github.com/tub-rip/event_collapse)
- <a name="Chamorro22ral"></a>Chamorro, W. O., Solà, J., Andrade-Cetto, J.,  
*[Event-based SLAM in real-time](https://doi.org/10.1109/LRA.2022.3187266)*,  
IEEE Robotics and Automation Letters, 7(3):8146-8153, 2022. [PDF](https://www.iri.upc.edu/download/scidoc/2608), [Video](http://www.iri.upc.edu/people/cetto/ral22_slam/22-0927_02_VI.mp4)
- <a name="Ji23arxiv"></a>Ji, X., Wei, J., Wang, Y., Shang, H., Kneip, L.,  
*[Cross-modal Place Recognition in Image Databases using Event-based Sensors](https://arxiv.org/abs/2307.01047)*,  
arXiv, 2023.
- <a name="Fischer22ral"></a>Fischer, T., Milford, M.,  
*[How Many Events Do You Need? Event-Based Visual Place Recognition Using Sparse But Varying Pixels](https://doi.org/10.1109/LRA.2022.3216226)*,    
IEEE Robotics and Automation Letters (RA-L), 7(4):12275-12282, 2022. [PDF](https://arxiv.org/pdf/2206.13673), [Code](https://github.com/Tobias-Fischer/sparse-event-vpr)

<a name="visual-servoing"></a>
### Visual Servoing
- <a name="Muthusamy21ieeeaccess"></a>Muthusamy, R., Ayyad, A., Halwani, M., Swart, D., Gan, D., Seneviratne, L., Zweiri, Y.,   
*[Neuromorphic Eye-in-Hand Visual Servoing](https://doi.org/10.1109/ACCESS.2021.3071261)*,  
IEEE Access, 2021. [YouTube](https://www.youtube.com/watch?v=g0R-mC43CX0).
- <a name="Muthusamy20arxiv"></a>Gomez Eguiluz, A., Rodriguez-Gomez, J.P., Martinez-de Dios, J.R., Ollero, A.,  
*[Asynchronous Event-based Line Tracking for Time-to-Contact Maneuvers in UAS](https://griffin-erc-advanced-grant.eu/wp-content/uploads/2020/08/Asynchronous-Event-based-Line-Tracking-for-Time-to-Contact-Maneuvers-in-UAS.pdf)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2020. [Youtube](https://www.youtube.com/watch?v=u9G_oT8psac&list=PL-Kzs2T7Hx3K-IDKsgUwPUnzHmk8Pcsek&index=4).
- <a name="Gomez21iros"></a>Gomez Eguiluz, A., Rodriguez-Gomez, J.P., Tapia, R., F.J., Maldonado, J.A., Acosta, J.R., Martinez-de Dios,  Ollero, A.,  
*[Why fly blind? Event-based visual guidance for ornithopter robot flight](https://doi.org/10.1109/IROS51168.2021.9636315)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021. [PDF](https://zenodo.org/record/5069951#.Yyx-oNJBxhF), [Youtube](https://www.youtube.com/watch?v=6aJJSEVStUE&list=PL-Kzs2T7Hx3K-IDKsgUwPUnzHmk8Pcsek).

<a name="slam-mapping"></a>
### Mapping
- See [Depth Estimation (3D Reconstruction)](#depth-estimation)

<a name="visual-odometry"></a>
### Visual Odometry / SLAM
#### Monocular
- [Cook et al., IJCNN 2011](#Cook11ijcnn),  
*Interacting maps for fast visual interpretation*. (Joint estimation of optical flow, image intensity and angular velocity with a rotating event camera).
- <a name="Weikersdorfer13icvs"></a>Weikersdorfer, D., Hoffmann, R., Conradt. J.,  
*[Simultaneous localization and mapping for event-based vision systems](http://doi.org/10.1007/978-3-642-39402-7_14)*.  
Int. Conf. Computer Vision Systems (ICVS), 2013, pp. 133-142. [PDF](https://mediatum.ub.tum.de/doc/1191908/271955.pdf), [Slides](http://workshops.acin.tuwien.ac.at/ICVS/downloads/ICVS2013-ebslam_weikersdorfer.pdf)
- [Kim et al., BMVC 2014](#Kim14bmvc),  
*Simultaneous Mosaicing and Tracking with an Event Camera*.
- <a name="Censi14icra"></a>Censi, A. and Scaramuzza, D.,  
*[Low-latency Event-based Visual Odometry](https://doi.org/10.1109/ICRA.2014.6906931)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2014, pp. 703-710. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA14_Censi.pdf), [Slides](https://censi.science/pub/research/2013-dvsd/201405-icra15-dvsd.pdf)
- <a name="Weikersdorfer14icra"></a>Weikersdorfer, D., Adrian, D. B., Cremers, D., Conradt, J.,  
*[Event-based 3D SLAM with a depth-augmented dynamic vision sensor](https://doi.org/10.1109/ICRA.2014.6906882)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2014, pp. 359-364.
    - [Weikersdorfer, Ph.D. Thesis, 2014](#Weikersdorfer14PhD),  
*Efficiency by Sparsity: Depth-Adaptive Superpixels and Event-based SLAM*.
- <a name="Kueng16iros"></a>Kueng, B., Mueggler, E., Gallego, G., Scaramuzza, D.,  
*[Low-Latency Visual Odometry using Event-based Feature Tracks](https://doi.org/10.1109/IROS.2016.7758089)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2016, pp. 16-23. [PDF](http://rpg.ifi.uzh.ch/docs/IROS16_Kueng.pdf). [YouTube](https://youtu.be/RDu5eldW8i8)
- <a name="Kim16eccv"></a>Kim, H., Leutenegger, S., Davison, A.J.,  
*[Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera](http://doi.org/10.1007/978-3-319-46466-4_21)*,  
European Conf. Computer Vision (ECCV), 2016, pp. 349-364. [PDF](https://www.doc.ic.ac.uk/~ajd/Publications/kim_etal_eccv2016.pdf), [YouTube](https://youtu.be/yHLyhdMSw7w), [YouTube 2](https://youtu.be/ajzdA4zkMfs)
- <a name="Gallego17ral"></a>Gallego, G. and Scaramuzza, D.,  
*[Accurate Angular Velocity Estimation with an Event Camera](https://doi.org/10.1109/LRA.2016.2647639)*,  
IEEE Robotics and Automation Letters (RA-L), 2(2):632-639, 2017.
[PDF](http://rpg.ifi.uzh.ch/docs/RAL16_Gallego.pdf),  [PPT](http://rpg.ifi.uzh.ch/docs/RAL16_Gallego.pptx),  [Youtube](https://youtu.be/v1sXWoOAs_0).
- <a name="Rebecq17ral"></a>Rebecq, H., Horstschaefer, T., Gallego, G., Scaramuzza, D.,  
*[EVO: A Geometric Approach to Event-based 6-DOF Parallel Tracking and Mapping in Real-time](https://doi.org/10.1109/LRA.2016.2645143)*,  
IEEE Robotics and Automation Letters (RA-L), 2(2):593-600, 2017. [PDF](http://rpg.ifi.uzh.ch/docs/RAL16_EVO.pdf),  [PPT](http://rpg.ifi.uzh.ch/docs/ICRA17_EVO.pptx),  [Poster](http://rpg.ifi.uzh.ch/docs/RAL16_EVO_poster.pdf),  [Youtube](https://youtu.be/bYqD2qZJlxE), [Code](https://github.com/uzh-rpg/rpg_dvs_evo_open).
- <a name="Reinbacher17iccp"></a>Reinbacher, C., Munda, G., Pock, T.,  
*[Real-Time Panoramic Tracking for Event Cameras](https://doi.org/10.1109/ICCPHOT.2017.7951488)*,  
IEEE Int. Conf. Computational Photography (ICCP), 2017. [PDF](https://arxiv.org/abs/1703.05161), [YouTube](https://youtu.be/Qy0brSlirmk), [Code](https://github.com/VLOGroup/dvs-panotracking)
- [Mueggler et al., IJRR 2017](#Mueggler17ijrr).  
*The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.*
- <a name="ZhuD19robio"></a>Zhu, D., Dong, J., Xu, Z., Ye, C., Hu, Y., Su, H., Liu, Z., Chen, G.,  
*[Neuromorphic Visual Odometry System for Intelligent Vehicle Application with Bio-inspired Vision Sensor](https://doi.org/10.1109/ROBIO49542.2019.8961878)*,  
IEEE Int. Conf. Robotics and Biomimetics (ROBIO), 2019. [PDF](https://arxiv.org/pdf/1909.02490)
- <a name="Park19iedm"></a>Park, P.K.J., Kim, J.-S., Shin, C.-W, Lee, H., Liu, W., Wang, Q., Roh, Y., Kim, J., Ater, Y., Soloveichik, E., Ryu, H. E.,  
*[Low-Latency Interactive Sensing for Machine Vision](https://doi.org/10.1109/IEDM19573.2019.8993468)*,  
IEEE Int. Electron Devices Meeting (IEDM), 2019.
- <a name="Liu20cvpr"></a>Liu, D., Parra, A., Chin, T.-J.,  
*[Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://arxiv.org/pdf/2002.10686)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [Project page](https://github.com/liudaqikk/CMBNB-demo)
- <a name="Gehrig20icra"></a>Gehrig, M., Shrestha, S. B., Mouritzen, D., Scaramuzza, D.,  
*[Event-Based Angular Velocity Regression with Spiking Networks](https://arxiv.org/pdf/2003.02790)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2020. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA20_Gehrig.pdf), [Code](https://github.com/uzh-rpg/snn_angular_velocity)
- <a name="Kim21ral"></a>Kim, H., Kim, H.J.,  
*[Real-Time Rotational Motion Estimation With Contrast Maximization Over Globally Aligned Events](https://doi.org/10.1109/LRA.2021.3088793)*,  
IEEE Robotics and Automation Letters (RA-L), 6(3):6016-6023, 2021. [Project page and Dataset](https://haram-kim.github.io/Globally_Aligned_Events/), [YouTube](https://youtu.be/wHeyIWEuEg4), [Code](https://github.com/Haram-kim/Globally_Aligned_Events)
- <a name="Liu21cvpr"></a>Liu, D., Parra, A., Chin, T.-J.,  
*[Spatiotemporal Registration for Event-Based Visual Odometry](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Spatiotemporal_Registration_for_Event-Based_Visual_Odometry_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Suppl. Mat.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Spatiotemporal_Registration_for_CVPR_2021_supplemental.pdf), [PDF](http://arxiv.org/abs/2103.05955), [Dataset](https://github.com/liudaqikk/RobotEvt)
- <a name="Wang22sensors"></a>Wang, Y., Yang, J., Peng, X., Wu, P., Gao, L., Huang, K., Chen, J., Kneip, L.,  
*[Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization](https://www.mdpi.com/1424-8220/22/15/5687)*,  
Sensors 22 (15), 5687.
- <a name="Zuo22icra"></a>Zuo, Y., Yang, J., Chen, J., Wang, X., Wang, Y., Kneip, L.,  
*[DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://arxiv.org/abs/2202.02556)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2022. [PDF](https://arxiv.org/abs/2202.02556), [YouTube](https://www.youtube.com/watch?v=RqAAA4MOqRI)
- <a name="Hidalgo22cvpr"></a>Hidalgo-Carrió J., Gallego, G., Scaramuzza, D.,  
*[EDS: Event-aided Direct Sparse Odometry](https://rpg.ifi.uzh.ch/docs/CVPR22_Hidalgo.pdf )*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [Poster](https://rpg.ifi.uzh.ch/docs/CVPR22_Hidalgo_poster.pdf),  [YouTube](https://youtu.be/Y97Sg4YTRh0),  [Project page and dataset](https://rpg.ifi.uzh.ch/eds.html), [Code](https://github.com/uzh-rpg/eds-buildconf)
- <a name="Gao23iccv"></a>Gao, L., Su, H., Gehrig, D., Cannici, M., Scaramuzza, D., Kneip, L.,  
*[A 5-Point Minimal Solver for Event Camera Relative Motion Estimation](https://openaccess.thecvf.com/content/ICCV2023/papers/Gao_A_5-Point_Minimal_Solver_for_Event_Camera_Relative_Motion_Estimation_ICCV_2023_paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2023. [Project Webpage](https://mgaoling.github.io/eventail_iccv23/), [PDF](https://arxiv.org/abs/2309.17054), [Poster](https://mgaoling.github.io/eventail_iccv23/files/poster.pdf), [Oral Presentation](https://www.youtube.com/watch?v=hyfGGzZQZh4), [Code](https://github.com/mgaoling/eventail_simulation_open)
    - <a name="Gao24cvpr"></a>Gao, L., Gehrig, D., Su, H., Scaramuzza, D., Kneip, L.,  
*[An N-Point Linear Solver for Line and Motion Estimation with Event Cameras](https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_An_N-Point_Linear_Solver_for_Line_and_Motion_Estimation_with_CVPR_2024_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2024. [Project Webpage](https://mgaoling.github.io/eventail/), [PDF](https://arxiv.org/abs/2404.00842), [Poster](https://mgaoling.github.io/eventail/files/poster.pdf), [Oral Presentation](https://www.youtube.com/watch?v=iHug6VjwPsw), [Code](https://github.com/mgaoling/eventail_simulation_open)
- <a name="Zuo24TRO"></a>Zuo, Y., Xu, W., Wang, X., Wang, Y., Kneip, L.,  
*[Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions](https://ieeexplore.ieee.org/document/10401990)*,  
IEEE Trans. Robot. (TRO), 2024. [PDF](https://arxiv.org/pdf/2401.08043.pdf), [Code](https://github.com/zyfff/Canny-EVT/)
- <a name="Xu243DV"></a>Xu, W., Zhang, S., Cui, L., Peng, X., Kneip, L.,  
*[Event-Based Visual Odometry on Non-Holonomic Ground Vehicles](https://arxiv.org/abs/2401.09331)*,  
IEEE Int. Conf. 3D Vision (3DV), 2024. [PDF](https://arxiv.org/pdf/2401.09331.pdf), [Code](https://github.com/gowanting/NHEVO), [YouTube](https://www.youtube.com/watch?v=gw5iJBQkk0M)
- <a name="Klenk23threedv"></a>Klenk, S., Motzet, M., Koestler, L., Cremers, D.,  
*[Deep Event Visual Odometry](https://arxiv.org/pdf/2312.09800)*,  
IEEE Int. Conf. 3D Vision (3DV), 2024. [Code](https://github.com/tum-vision/DEVO), [YouTube](https://youtu.be/rP_OuOE-O34)
- [Mollica et al., T-ITS 2024](#Mollica24tits),  
*MA-VIED: A Multisensor Automotive Visual Inertial Event Dataset.*
- <a name="Guo24tro"></a>Guo, S. and Gallego, G.,  
*[CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://arxiv.org/pdf/2403.08119)*,  
IEEE Trans. Robot. (TRO), 2024. [Project page](https://github.com/tub-rip/cmax_slam), [YouTube](https://youtu.be/17VWTuSkgPs), [ECRot Dataset](https://github.com/tub-rip/ECRot)
- [Shiba et al. TPAMI 2024](#Shiba24tpami),  
*Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization*.

#### Stereo
- <a name="Zhou21tro"></a>Zhou, Y., Gallego, G., Shen, S.,  
*[ESVO: Event-based Stereo Visual Odometry](https://doi.org/10.1109/TRO.2021.3062252)*,  
IEEE Trans. Robot. (TRO), 2021. [Project page](https://sites.google.com/view/esvo-project-page/home), [PDF](https://arxiv.org/pdf/2007.15548), [YouTube](https://youtu.be/3CPPs1gz04k), [Code](https://github.com/HKUST-Aerial-Robotics/ESVO.git).
- [Xiao et al., arXiv 2021](#Xiao22icrmv),  
*Research on Event Accumulator Settings for Event-Based SLAM*.
- [Shiba et al. TPAMI 2024](#Shiba24tpami),  
*Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization*.

<a name="visual-inertial"></a>
### Visual-Inertial Odometry
- <a name="Mueggler18tro"></a>Mueggler, E., Gallego, G., Rebecq, H., Scaramuzza, D.,  
*[Continuous-Time Visual-Inertial Odometry for Event Cameras](http://rpg.ifi.uzh.ch/docs/TRO18_Mueggler.pdf)*,  
IEEE Trans. Robot. (TRO), 2018.
- [Mueggler et al., IJRR 2017](#Mueggler17ijrr).  
*The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.*
- <a name="Zhu17cvpr"></a>Zhu, A., Atanasov, N., Daniilidis, K.,  
*[Event-based Visual Inertial Odometry](https://doi.org/10.1109/CVPR.2017.616)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017. [PDF](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhu_Event-Based_Visual_Inertial_CVPR_2017_paper.pdf), [Supplementary material](http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhu_Event-Based_Visual_Inertial_2017_CVPR_supplemental.zip), [YouTube](https://youtu.be/9zGoR67l9Wc), [YouTube 2](https://youtu.be/X3QIFj5Qc4A)
- <a name="Rebecq17bmvc"></a>Rebecq, H., Horstschaefer, T., Scaramuzza, D.,  
*[Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization](http://rpg.ifi.uzh.ch/docs/BMVC17_Rebecq.pdf)*,  
British Machine Vision Conf. (BMVC), 2017. [PDF](http://rpg.ifi.uzh.ch/docs/BMVC17_Rebecq.pdf), [Appendix](http://rpg.ifi.uzh.ch/docs/BMVC17_Rebecq_appendix.pdf), [YouTube](https://youtu.be/F3OFzsaPtvI), [Project page](http://rpg.ifi.uzh.ch/ultimateslam.html), [PPT](http://rpg.ifi.uzh.ch/docs/BMVC17_Rebecq.pptx), [Oral presentation](https://youtu.be/iYptNMqK0tQ).
- <a name="Rosinol18ral"></a>Rosinol Vidal, A., Rebecq, H., Horstschaefer, T., Scaramuzza, D.,  
*[Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://doi.org/10.1109/LRA.2018.2793357)*,  
IEEE Robotics and Automation Letters (RA-L), 3(2):994-1001, Apr. 2018. [PDF](http://rpg.ifi.uzh.ch/docs/RAL18_VidalRebecq.pdf), [YouTube](https://youtu.be/jIvJuWdmemE), [Poster](http://rpg.ifi.uzh.ch/docs/RAL18_VidalRebecq_poster.pdf), [Project page](http://rpg.ifi.uzh.ch/ultimateslam.html), [ICRA18 video pitch](https://youtu.be/0hDGFFJQfmA).
- [Nelson, K. J., MSc Thesis 2019](#Nelson19MSc),  
*Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle*.
- [Rebecq et al., TPAMI 2020](#Rebecq20tpami),  
*High Speed and High Dynamic Range Video with an Event Camera*.
    - [Rebecq et al., CVPR 2019](#Rebecq19cvpr),  
*Events-to-Video: Bringing Modern Computer Vision to Event Cameras*.
- [Friedel, Z. P., MSc Thesis 2020](#Friedel20MSc),  
*Event-Based Visual-Inertial Odometry Using Smart Features*.
- <a name="LeGentil20arxiv"></a>Le Gentil, C., Tschopp, F., Alzugaray, I., Vidal-Calleja, T., Siegwart, R., Nieto, J.,  
*[IDOL: A Framework for IMU-DVS Odometry using Lines](https://ras.papercept.net/proceedings/IROS20/1135.pdf)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2020. [PDF](https://arxiv.org/pdf/2008.05749)
- <a name="Xiao22icrmv"></a>Xiao, K., Wang, G., Chen, Y., Xie, Y., Li, H.,  
*[Research on Event Accumulator Settings for Event-Based SLAM](https://arxiv.org/abs/2112.00427)*,  
arXiv, 2021. [PDF(Monocular VIO ony)](https://arxiv.org/abs/2112.00427), [Code (VIO and Stereo)](https://github.com/robin-shaun/event-slam-accumulator-settings).
- <a name="Mahlknecht22ral"></a>Mahlknecht, F., Gehrig, D., Nash, J., Rockenbauer, F., Morrell, B., Delaune, J., Scaramuzza, D.,  
*[Exploring Event Camera-based Odometry for Planetary Robots](http://dx.doi.org/10.1109/LRA.2022.3187826)*,  
IEEE Robotics and Automation Letters (RA-L),2022. [PDF](https://rpg.ifi.uzh.ch/docs/RAL22_Mahlknecht.pdf), [code&dataset](https://uzh-rpg.github.io/eklt-vio/), [YouTube](https://youtu.be/GvKfJrMGNCs).  
- <a name="Chamorro23cvprworkshop"></a>Chamorro, W. O., Solà, J., Andrade-Cetto, J.,  
*[Event-IMU fusion strategies for faster-than-IMU estimation throughput](https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_Event-IMU_fusion_strategies_for_faster-than-IMU_estimation_throughput.pdf)*,  
CVPR 2023 Workshop on Event-based Vision. [PDF](https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_Event-IMU_fusion_strategies_for_faster-than-IMU_estimation_throughput.pdf), [Video](https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_Event-IMU_fusion_strategies_for_faster-than-IMU_estimation_throughput_supp.zip).
- <a name="Xu23TRO"></a>Xu, W., Peng, X., Kneip, L.,  
*[Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation](https://ieeexplore.ieee.org/abstract/document/10319103/)*,  
IEEE Trans. Robot. (TRO), 2023. [PDF](https://arxiv.org/pdf/2401.09296v1.pdf)
- [Mollica et al., T-ITS 2024](#Mollica24tits),  
*MA-VIED: A Multisensor Automotive Visual Inertial Event Dataset.*

#### Stereo
- <a name="Chen22arxiv"></a>Chen, P., Guan, W., Lu, P.,  
*[ESVIO: Event-based Stereo Visual Inertial Odometry](https://arxiv.org/abs/2212.13184)*,  
arXiv, 2022.


<a name="segmentation"></a>
## Segmentation

<a name="object-segmentation"></a>
### Object Segmentation
- <a name="Barranco15iccv"></a>Barranco, F., Teo, C. L., Fermüller, C., Aloimonos, Y.,  
*[Contour Detection and Characterization for Asynchronous Event Sensors](https://doi.org/10.1109/ICCV.2015.63)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2015, pp. 486-494. [PDF](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Barranco_Contour_Detection_and_ICCV_2015_paper.pdf), [Project page](http://www.umiacs.umd.edu/research/POETICON/DVSContours/)
- <a name="Marcireau18fnins"></a>Marcireau, A., Ieng, S.-H., Simon-Chane, C., Benosman, R.,  
*[Event-Based Color Segmentation With a High Dynamic Range Sensor](https://doi.org/10.3389/fnins.2018.00135)*.  
Front. Neurosci. (2018), 12:135. [PDF](https://www.neuromorphic-vision.com/public/publications/53/publication.pdf)
- <a name="Barranco18iros"></a>Barranco, F., Fermüller, C., Ros, E.,  
*[Real-Time Clustering and Multi-Target Tracking Using Event-Based Sensors](https://doi.org/10.1109/IROS.2018.8593380)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018. [PDF](https://arxiv.org/pdf/1807.02851.pdf), [YouTube](https://youtu.be/p-eapPg5BuE)
- <a name="Alonso19cvprw"></a>Alonso I., Murillo A.,  
*[EV-SegNet: Semantic Segmentation for Event-based Cameras](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Alonso_EV-SegNet_Semantic_Segmentation_for_Event-Based_Cameras_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [PDF](https://arxiv.org/pdf/1811.12039.pdf). [Project page](https://github.com/Shathe/Ev-SegNet). [Video pitch](https://youtu.be/AuXN7y3bMqo)
- <a name="Wang21cvpr"></a>Wang, L., Chae, Y., Yoon, S.-H., Kim, T.-K., Yoon, K.-J.,  
*[EvDistill: Asynchronous Events To End-Task Learning via Bidirectional Reconstruction-Guided Cross-Modal Knowledge Distillation](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Code](https://github.com/addisonwang2013/evdistill).
- <a name="Sun22eccv"></a>Sun, Z., Messikommer, N., Gehrig, D., Scaramuzza, D.,  
*[ESS: Learning Event-based Semantic Segmentation from Still Images](http://dx.doi.org/10.1007/978-3-031-19830-4_20)*,  
European Conf. Computer Vision (ECCV), 2022. [PDF](https://rpg.ifi.uzh.ch/docs/ECCV22_Sun.pdf), [dataset](https://dsec.ifi.uzh.ch/dsec-semantic/), [YouTube](https://youtu.be/Tby5c9IDsDc), [code](https://github.com/uzh-rpg/ess).  
- <a name="Biswas23wacv"></a>Biswas, S.D., Kosta, A.,  Liyanagedera, C., Apolinario, M., Roy, K.,  
*[HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities](https://arxiv.org/abs/2211.10754)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2024.  
- <a name="Chen24cvpr"></a>Chen, Z., Zhu, Z., Hou, J., Shi, G., Wu, J.,  
*[Segment Any Event Stream via Weighted Adaptation of Pivotal Tokens](https://arxiv.org/abs/2312.16222)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2024. [Code](https://github.com/happychenpipi/EventSAM).

<a name="motion-segmentation"></a>
### Motion Segmentation
- [Glover et al., IROS 2016](#Glover16iros),  
*Event-driven ball detection and gaze fixation in clutter*.
    - [Glover et al., IROS 2017](#Glover17iros),  
*Robust Visual Tracking with a Freely-moving Event Camera*.
- <a name="Mishra17fnins"></a>Mishra, A., Ghosh, R., Principe, J.C., Thakor, N.V., Kukreja, S.L.,  
*[A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors](https://doi.org/10.3389/fnins.2017.00083)*,  
Front. Neurosci. (2017), 11:83.
- <a name="Vasco17icar"></a>Vasco, V., Glover, A., Mueggler, E., Scaramuzza, D., Natale, L., Bartolozzi, C.  
*[Independent Motion Detection with Event-driven Cameras](http://doi.org/10.1109/ICAR.2017.8023661)*,  
Int. Conf. Advanced Robotics (ICAR), 2017, pp. 530-536. [PDF](https://arxiv.org/pdf/1706.08713v2.pdf)
- [Stoffregen et al., ACRA 2017](#Stoffregen17acra),  
*Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor*.
- [Mitrokhin et al., IROS 2018](#Mitrokhin18iros),  
*Event-based Moving Object Detection and Tracking*.
- <a name="Stoffregen19iccv"></a>Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., Scaramuzza, D.,  
*[Event-Based Motion Segmentation by Motion Compensation](http://rpg.ifi.uzh.ch/docs/ICCV19_Stoffregen.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2019. [PDF (animations best viewed with Acrobat Reader)](http://rpg.ifi.uzh.ch/docs/ICCV19_Stoffregen.pdf), [YouTube](https://youtu.be/0q6ap_OSBAk)
- [Mitrokhin et al., IROS 2019](#Mitrokhin19iros),  
*EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras*.  
- <a name="Mitrokhin20cvpr"></a>Mitrokhin, A., Hua, Z., Fermüller, C., Aloimonos, Y.,  
*[Learning Visual Motion Segmentation Using Event Surfaces](http://openaccess.thecvf.com/content_CVPR_2020/html/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [PDF](http://openaccess.thecvf.com/content_CVPR_2020/papers/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.pdf), [Suppl. Mat](http://openaccess.thecvf.com/content_CVPR_2020/supplemental/Mitrokhin_Learning_Visual_Motion_CVPR_2020_supplemental.pdf)
- <a name="Parameshwara20arxiv"></a>Parameshwara, C. M., Sanket, N. J., Gupta, A., Fermüller, C., Aloimonos, Y.,  
*[MOMS with Events: Multi-Object Motion Segmentation With Monocular Event Cameras](https://arxiv.org/abs/2006.06158)*,  
arXiv, 2020.
- [Kepple et al., ECCV 2020](#Kepple20eccv),  
*Jointly learning visual motion and confidence from local patches in event cameras*.
- <a name="Zhou21tnnls"></a>Zhou, Y., Gallego, G., Lu, X., Liu, S., Shen, S.,  
*[EMSGC: Event-based Motion Segmentation with Spatio-Temporal Graph Cuts](https://arxiv.org/pdf/2012.08730)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 2021.  [Project page](https://sites.google.com/view/emsgc),  [YouTube](https://youtu.be/ztUyNlKUwcM), [Code](https://github.com/HKUST-Aerial-Robotics/EMSGC.git).
- <a name="Wang23arxiva"></a>Wang, Z., Guo, J., Daniilidis, K.,  
*[Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation](https://arxiv.org/pdf/2312.00114.pdf)*,  
arxiv, 2023.  [Project page](https://www.cis.upenn.edu/~ziyunw/un_evmoseg/).
- <a name="Jiang24visapp"></a>Jiang, C., Moreau, J., Davoine, F.,  
*[Event-based Semantic-aided Motion Segmentation](https://doi.org/10.5220/0012308100003660)*,  
Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2024.
- [Shiba et al. TPAMI 2024](#Shiba24tpami),  
*Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization*.

<a name="pattern-recognition"></a>
## Pattern Recognition

<a name="object-recognition"></a>
### Object Recognition
- <a name="SerranoGotarredona09tnn"></a>Serrano-Gotarredona, R., Oster, M., Lichtsteiner, P., Linares-Barranco, A., Paz-Vicente, R., Gómez-Rodríguez, F., Camuñas-Mesa, L., Berner, R., Rivas, M., Delbrück, T., Liu, S. C., Douglas, R., Häfliger, P., Jiménez-Moreno, G., Civit, A., Serrano-Gotarredona, T., Acosta-Jiménez, A., Linares-Barranco, B.,  
*[CAVIAR: A 45k-Neuron, 5M-Synapse, 12G-connects/sec AER Hardware Sensory-Processing-Learning-Actuating System for High Speed Visual Object Recognition and Tracking](https://doi.org/10.1109/TNN.2009.2023653)*,  
IEEE Trans. on Neural Netw., 20(9):1417-1438, 2009. [PDF](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.192.1326&rep=rep1&type=pdf)
- <a name="Belbachir11tie"></a>Belbachir, A. N., Hofstaetter, M., Litzenberger, M., Schoen, P.,  
*[High Speed Embedded Object Analysis Using a Dual-Line Timed-Address-Event Temporal Contrast Vision Sensor](https://doi.org/10.1109/TIE.2010.2095390)*,  
IEEE Trans. Ind. Electron., 58(3):770-783, 2011.
- <a name="Ghosh14biocas"></a>Ghosh, R., Mishra, A., Orchard, G., Thakor, N.,  
*[Real-time object recognition and orientation estimation using an event-based camera and CNN](https://doi.org/10.1109/BioCAS.2014.6981783)*,  
IEEE Biomedical Circuits and Systems Conf. (BioCAS), 2014.
- <a name="SerranoGotarredona15iscas"></a>Serrano-Gotarredona, T., Linares-Barranco, B., Galluppi, F., Plana, L., Furber, S.,  
*[ConvNets experiments on SpiNNaker](https://doi.org/10.1109/ISCAS.2015.7169169)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
- <a name="Cohen16fnins"></a>Cohen, G., Orchard, G., Ieng, S.-H., Tapson, J., Benosman, R., van Schaik, A.,  
*[Skimming Digits: Neuromorphic Classification of Spike-Encoded Images](https://doi.org/10.3389/fnins.2016.00184)*,  
Front. Neurosci. (2016), 10:184. [PDF](https://www.neuromorphic-vision.com/public/publications/45/publication.pdf)
- [Barua et al., WACV 2016](#Barua16wacv),  
*Direct face detection and video reconstruction from event cameras*.
- <a name="Moeys16ebccsp"></a>Moeys, D., Corradi F., Kerr, E., Vance, P., Das, G., Neil, D., Kerr, D., Delbruck, T.,  
*[Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network](https://doi.org/10.1109/EBCCSP.2016.7605233)*,  
IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2016. [PDF](https://arxiv.org/pdf/1606.09433.pdf), [YouTube 1](https://youtu.be/fL3YCIPxuhM), [YouTube 2](https://youtu.be/lPF3Youpmqk), [YouTube 3](https://youtu.be/zmiosSxT_DU)
- <a name="Li16bics"></a>Li, H., Li, G., Shi, L.,  
*[Classification of Spatiotemporal Events Based on Random Forest](https://doi.org/10.1007/978-3-319-49685-6_13)*,  
Int. Conf. Brain Inspired Cognitive Systems (BICS), 2016.
- <a name="Ghosh16icann"></a>Ghosh, R., Siyi, T., Rasouli, M., Thakor, N. V., Kukreja, S. L.,    
*[Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM](https://doi.org/10.1007/978-3-319-44781-0_54)*,  
Int. Conf. Artificial Neural Networks (ICANN), 2016. [PDF](https://arxiv.org/pdf/1903.07873)
- <a name="Cohen18tnnls"></a>Cohen, G., Afshar, S., Orchard, G., Tapson, J., Benosman, R., van Schaik, A.,  
*[Spatial and Temporal Downsampling in Event-Based Visual Classification](https://doi.org/10.1109/TNNLS.2017.2785272)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 29(10):5030-5044, Oct. 2018.
- <a name="Lenz20fnins"></a>Lenz, G., Ieng, S.-H., Benosman, R.,  
*[Event-Based Face Detection and Tracking Using the Dynamics of Eye Blinks](https://doi.org/10.3389/fnins.2020.00587)*,  
Front. Neurosci., 2020. [PDF](http://arxiv.org/abs/1803.10106), [YouTube](https://www.youtube.com/watch?v=F5UzXQsr5Es)
- <a name="Ramesh18accvw"></a>Ramesh, B., Ussa, A., Della Vedova, L., Yang, H., Orchard, G.,  
*[PCA-RECT: An Energy-Efficient Object Detection Approach for Event Cameras](https://doi.org/10.1007/978-3-030-21074-8_35)*,  
Assian Conf. Computer Vision Workshops (ACCVW), 2018. [Code](https://github.com/nusneuromorphic/PCA-RECT)
    - <a name="Ramesh20fnins"></a>Ramesh, B., Ussa, A., Della Vedova, L., Yang, H., Orchard, G.,  
*[Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras](https://doi.org/10.3389/fnins.2020.00135)*,  
Front. Neurosci. (2020), 14:135. [N-SOD Dataset](https://drive.google.com/drive/folders/10_bnZ_TOcq7xCtCiDcaDiIO3_Txgr19S)
- <a name="Negri18icecs"></a>Negri, P., Soto, M., Linares-Barranco, B., Serrano-Gotarredona, T.,  
*[Scene Context Classification with Event-Driven Spiking Deep Neural Networks](https://dx.doi.org/10.1109/ICECS.2018.8617982)*,  
IEEE Int. Conf. Electronics, Circuits and Systems (ICECS), 2018.
- <a name="Cannici19wacv"></a>Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.,  
*[Attention Mechanisms for Object Recognition With Event-Based Cameras](https://arxiv.org/abs/1807.09480)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2019.
- <a name="Jiang19icra"></a> Jiang, Z., Xia, P., Huang, K., Stechele, W., Chen, G., Bing, Z., Knoll, A.,  
*[Mixed Frame-/Event-Driven Fast Pedestrian Detection](https://doi.org/10.1109/ICRA.2019.8793924)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2019.
- <a name="Rueckauer19arxiv"></a>Rueckauer, B., Kanzig, N., Liu, S.-C., Delbruck, T., Sandamirskaya, Y.,  
*[Closing the Accuracy Gap in an Event-Based Visual Recognition Task](https://arxiv.org/pdf/1906.08859.pdf)*,  
arXiv, 2019.
- <a name="Bi19iccv"></a>Bi, Y., Chadha, A., Abbas, A.,  Bourtsoulatze, E., Andreopoulos, Y.,  
*[Graph-Based Object Classification for Neuromorphic Vision Sensing](http://openaccess.thecvf.com/content_ICCV_2019/html/Bi_Graph-Based_Object_Classification_for_Neuromorphic_Vision_Sensing_ICCV_2019_paper.html),*  
IEEE Int. Conf. Computer Vision (ICCV), 2019.
[PDF](http://openaccess.thecvf.com/content_ICCV_2019/papers/Bi_Graph-Based_Object_Classification_for_Neuromorphic_Vision_Sensing_ICCV_2019_paper.pdf), [Suppl. Mat.](http://openaccess.thecvf.com/content_ICCV_2019/supplemental/Bi_Graph-Based_Object_Classification_ICCV_2019_supplemental.pdf), [PDF arXiv](https://arxiv.org/pdf/1908.06648.pdf), [Github Page](https://github.com/PIX2NVS/NVS2Graph), [ASL-DVS Dataset](https://www.dropbox.com/sh/ibq0jsicatn7l6r/AACNrNELV56rs1YInMWUs9CAa?dl=0).
    - [Bi et al arXiv 2019](#Bi19arxiv),  
*Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing*.
- <a name="Li19icme"></a>Li, J., Dong, S., Yu, Z., Tian, Y., Huang, T.,  
*[Event-Based Vision Enhanced: A Joint Detection Framework in Autonomous Driving](https://doi.org/10.1109/ICME.2019.00242),*  
IEEE Int. Conf. Multimedia and Expo (ICME), 2019.
- <a name="Iacono19iros"></a>Iacono, M., D'Angelo, G., Glover, A., Tikhanoff, V., Niebur, E., Bartolozzi, C.,  
*[Proto-Object Based Saliency for Event-Driven Cameras](https://doi.org/10.1109/IROS40897.2019.8967943)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2019.
- <a name="Gao19iccvw"></a>Gao, S., Guo, G., Chen, C.L.P.,  
*[Event-Based Incremental Broad Learning System for Object Classification](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CEFRL/Gao_Event-Based_Incremental_Broad_Learning_System_for_Object_Classification_ICCVW_2019_paper.pdf),*  
IEEE Int. Conf. Computer Vision Workshop (ICCVW), 2019.
    - <a name="Gao20access"></a>Gao, S., Guo, G., Huang, H., Cheng, X., Chen, C.L.P.,  
*[An End-to-End Broad Learning System for Event-Based Object Classification](https://doi.org/10.1109/ACCESS.2020.2978109)*,  
IEEE Access, 8:45974-45984, 2020.
- <a name="Nan19ssci"></a>Nan, Y., Xiao, R., Gao, S., Yan, R.,  
*[An Event-based Hierarchy Model for Object Recognition](https://www.researchgate.net/publication/336318915_An_Event-based_Hierarchy_Model_for_Object_Recognition)*,  
IEEE Symp. Series in Computational Intell. (SSCI), 2019.
- <a name="Damien19visigrapp"></a>Damien, J., Hubert, K., Frederic, C.,  
*[Convolutional Neural Network for Detection and Classification with Event-based Data](https://www.scitepress.org/PublicationsDetail.aspx?ID=gn69gKYUbaM%3d&t=1)*,  
Int. Joint Conf. Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2019. [PDF](https://pdfs.semanticscholar.org/0453/c19ced72b649e0609f04cd861a75140ed734.pdf).
- <a name="Xiao19tnnls"></a>Xiao, R., Tang, H., Ma, Y., Yan, R., Orchard, G.,  
*[An Event-Driven Categorization Model for AER Image Sensors Using Multispike Encoding and Learning](https://doi.org/10.1109/TNNLS.2019.2945630)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 2019.
- <a name="Cheng19fnbot"></a>Chen, G., Cao, H., Ye, C., Zhang, Z., Liu, X., Mo, X., Qu, Z., Conradt, J., Röhrbein, F., Knoll, A.,  
*[Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors](https://doi.org/10.3389/fnbot.2019.00010)*,  
Front. Neurorobot. 13:10, 2019.
- [Li and Shi, Front. Neurorobot. 2019](#Li19fnbot),  
*Robust Event-Based Object Tracking Combining Correlation Filter and CNN Representation*.
- <a name="CamunasMesa19icecs"></a>Camuñas-Mesa, L.A., Linares-Barranco, B., Serrano-Gotarredona, T.,  
*[Low-power hardware implementation of SNN with decision block for recognition tasks](https://doi.org/10.1109/ICECS46596.2019.8964964)*,  
IEEE Int. Conf. Electronics, Circuits and Systems (ICECS), 2019.
- [Park et al., IEDM 2019](#Park19iedm),  
*Low-Latency Interactive Sensing for Machine Vision*.
- <a name="Liu20aaai"></a>Liu, Q., Ruan, H., Xing, D., Tang, H., Pan, G.,  
*[Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks](https://arxiv.org/pdf/2002.06199)*,  
AAAI Conf. Artificial Intelligence, 2020.
- <a name="Ramesh20wacvw"></a>Ramesh, B., Yang, H.,  
*[Boosted Kernelized Correlation Filters for Event-based Face Detection](http://openaccess.thecvf.com/content_WACVW_2020/papers/w5/Ramesh_Boosted_Kernelized_Correlation_Filters_for_Event-based_Face_Detection_WACVW_2020_paper.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision Workshops (WACVW), 2020.
- <a name="Giannone20arxiv"></a>Giannone, G., Anoosheh, A., Quaglino, A., D'Oro, P., Gallieri, M., Masci, J.,  
*[Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs](https://arxiv.org/pdf/2004.03156)*,  
arXiv, 2020.
- <a name="Bisulco20isvlsi"></a>A. Bisulco, F. Cladera Ojeda, V. Isler and D. D. Lee,,  
*[Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection](https://ieeexplore.ieee.org/document/9155035)*,  
IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2020.
- <a name="Lu20access"></a>Lu, J., Dong, J., Yan, R., Tang, H.,  
*[An Event-based Categorization Model Using Spatio-temporal Features in a Spiking Neural Network](https://doi.org/10.1109/ICACI49185.2020.9177628)*,  
IEEE Int. Conf. Advanced Computational Intelligence (ICACI), 2020.
- <a name="Perot20nips"></a>Perot, E., de Tournemire, P., Nitti, D., Masci, J., Sironi, A.,  
[Learning to Detect Objects with a 1 Megapixel Event Camera](https://papers.nips.cc/paper/2020/file/c213877427b46fa96cff6c39e837ccee-Paper.pdf),  
Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. [1Mpx Detection Dataset](#1mpx_detection_dataset)
- <a name="Ojeda20icip"></a>Ojeda, F. C., Bisulco, A., Kepple, D., Isler, V., Lee, D. D.,  
*[On-Device Event Filtering with Binary Neural Networks for Pedestrian Detection Using Neuromorphic Vision Sensors](https://doi.org/10.1109/ICIP40778.2020.9191148)*,  
IEEE Int. Conf. Image Processing (ICIP), 2020.
- <a name="Deng20amae"></a>Deng, Y., Li, Y., Chen, H.,  
*[AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification](http://ras.papercept.net/images/temp/IROS/files/2483.pdf)*,  
IEEE Robotics and Automation Letters (RA-L), 5(3):4596-4603, July 2020.
- [Baldwin et al., TPAMI 2022](#Baldwin22tpami).  
*Time-Ordered Recent Event (TORE) Volumes for Event Cameras*.
- <a name="Patel21abec"></a>Patel, H., Iaboni, C., Lobo, D., Choi, J., Abichandani, P.,  
*[Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots](https://arxiv.org/pdf/2102.11916v1)*,  
arXiv, 2021.
- <a name="Kim21iccv"></a>Kim, J., Bae, J., Park, G., Zhang, D., and Kim, Y.,  
*[N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_N-ImageNet_Towards_Robust_Fine-Grained_Object_Recognition_With_Event_Cameras_ICCV_2021_paper.pdf),*  
IEEE Int. Conf. Computer Vision (ICCV), 2021.
[Suppl. Mat.](https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_N-ImageNet_Towards_Robust_ICCV_2021_supplemental.pdf), [Github Page](https://github.com/82magnolia/n_imagenet), [N-ImageNet Dataset](https://www.dropbox.com/sh/47we7z2gff5barh/AADU4GyWnzLFzMzBDjLP00baa?dl=0).
- <a name="li21iccv"></a>Li, Y., Zhou, H., Yang, B., Zhang, Y., Cui, Z., Bao, H., Zhang, G.,  
*[Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://openaccess.thecvf.com/content/ICCV2021/html/Li_Graph-Based_Asynchronous_Event_Processing_for_Rapid_Object_Recognition_ICCV_2021_paper.html)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2021. [PDF](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Graph-Based_Asynchronous_Event_Processing_for_Rapid_Object_Recognition_ICCV_2021_paper.pdf), [Suppl.](https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Graph-Based_Asynchronous_Event_ICCV_2021_supplemental.pdf)
- <a name="wang22icip"></a>Wang, Z., Hu, Y., Liu, S.-C,  
*[Exploiting Spatial Sparsity for Event Cameras with Visual Transformers](https://ieeexplore.ieee.org/document/9897432)*,  
IEEE Int. Conf. Image Processing (ICIP), 2022. [PDF](https://arxiv.org/pdf/2202.05054.pdf).
- <a name="Gehrig23cvpr"></a>Gehrig, M., Scaramuzza, D.,  
*[Recurrent Vision Transformers for Object Detection with Event Cameras](https://arxiv.org/abs/2212.05598)*,  
 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR23_mgehrig.pdf), [Code](https://github.com/uzh-rpg/RVT).
- [El Shair et al., IEEE Access 2023](#Elshair23access).  
*CSTR: A Compact Spatio-Temporal Representation for Event-Based Vision*.
- <a name="Himmi24wacv"></a>Himmi, S., Parret, V., Chhatkuli , A., Van Gool, L.,  
*[MS-EVS: Multispectral event-based vision for deep learning based face detection](https://openaccess.thecvf.com/content/WACV2024/papers/Himmi_MS-EVS_Multispectral_Event-Based_Vision_for_Deep_Learning_Based_Face_Detection_WACV_2024_paper.pdf)*,  
 IEEE Winter Conf. Applications of Computer Vision (WACV), 2024. [Dataset](https://ms-evs.github.io/), [Poster](https://github.com/ms-evs/ms-evs.github.io/blob/40d657cce1f60f32b6745c5514daeeef5d18d1aa/wacv24-poster.pdf).
- <a name="Zhang24neurocomputing"></a>Zhang, P., Wang, C., Lam, E. Y.,  
*[Neuromorphic Imaging and Classification with Graph Learning](https://doi.org/10.1016/j.neucom.2023.127010)*,  
Neurocomputing, 2024. [PDF](https://arxiv.org/pdf/2309.15627.pdf), [Code](https://github.com/pz-even/ni-graph)
- <a name="Zubic24cvpr"></a>Zubić, N., Gehrig, M., Scaramuzza, D.,  
*[State Space Models for Event Cameras](https://arxiv.org/abs/2402.15584)*,  
 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2024. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR2024_Zubic.pdf), [Code](https://github.com/uzh-rpg/ssms_event_cameras).

<a name="gesture-recognition"></a>
### Gesture Recognition
- <a name="Lee14tnnls"></a>Lee, J. H., Delbruck, T., Pfeiffer, M., Park, P.K.J., Shin, C.-W., Ryu, H., Kang, B. C.,  
*[Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas](https://doi.org/10.1109/TNNLS.2014.2308551)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 25(12):2250-2263, 2014.
    - <a name="Lee12iscas"></a>Lee, J., Delbruck, T., Park, P.K.J., Pfeiffer, M., Shin, C. W., Ryu, H., Kang, B. C.,  
*[Live demonstration: Gesture-Based remote control using stereo pair of dynamic vision sensors](https://doi.org/10.1109/ISCAS.2012.6272144)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2012. [PDF](http://www.zora.uzh.ch/75315/1/Lee_et_al_Live_demonstration.pdf), [YouTube](https://youtu.be/IlKimfJN21A)
- <a name="Kohn12iscas"></a>Kohn, B., Belbachir, A.N., Hahn, T., Kaufmann, H.,  
*[Event-driven Body Motion Analysis For Real-time Gesture Recognition](https://doi.org/10.1109/ISCAS.2012.6272132)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2012. [PDF](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.839.4932&rep=rep1&type=pdf)
    - <a name="Kohn12cvprw"></a>Kohn, B., Belbachir, A.N., Nowakowska, A.,  
*[Real-time Gesture Recognition using a Bio-inspired 3D Vision Sensor](https://doi.org/10.1109/CVPRW.2012.6239184)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
    - <a name="Kohn12cvprw2"></a>Kohn, B., Nowakowska, A., Belbachir, A.N.,  
*[Real-time Body Motion Analysis for Dance Pattern Recognition](https://doi.org/10.1109/CVPRW.2012.6238894)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
- <a name="Orchard15tpami"></a>Orchard, G., Meyer, C., Etienne-Cummings, R., Posch, C., Thakor, N., Benosman, R.,  
*[HFIRST: A Temporal Approach to Object Recognition](https://doi.org/10.1109/TPAMI.2015.2392947)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 37(10):2028-2040, 2015. [PDF](https://arxiv.org/pdf/1508.01176.pdf), [PDF](https://www.neuromorphic-vision.com/public/publications/36/publication.pdf)
    - [Code](https://github.com/gorchard/HFIRST): HFIRST: A simple spiking neural network for recognition based on the canonical frame-based HMAX model.
- <a name="Park15icip"></a>Park, P.K.J., Lee, K., Lee, J.H., Kang, B., Shin, C.-W., Woo, J., Kim, J.-S., Suh, Y., Kim, S., Moradi, S., Gurel, O., Ryu, H.,  
*[Computationally efficient, real-time motion recognition based on bio-inspired visual and cognitive processing](http://dx.doi.org/10.1109/ICIP.2015.7350936)*,  
IEEE Int. Conf. Image Processing (ICIP), 2015.
- <a name="Park16icip"></a>Park, P.K.J., Cho, B.H., Park, J.M., Lee, K., Kim, H.Y., Kang, H.A., Lee, H.G., Woo, J., Roh, Y., Lee, W.J., Shin, C.-W., Wang, Q., Ryu, H.,  
*[Performance improvement of deep learning based gesture recognition using spatiotemporal demosaicing technique](http://dx.doi.org/10.1109/ICIP.2016.7532633)*,  
IEEE Int. Conf. Image Processing (ICIP), 2016.
- <a name="Lungu17iscas"></a>Lungu, I.-A., Corradi, F., Delbruck, T.,  
*[Live Demonstration: Convolutional Neural Network Driven by Dynamic Vision Sensor Playing RoShamBo](https://doi.org/10.1109/ISCAS.2017.8050403)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2017. [YouTube](https://youtu.be/q5ua91n13TA), [Slides 36-39](http://rpg.ifi.uzh.ch/docs/ICRA17workshop/Delbruck.pdf)
- <a name="Amir17cvpr"></a>Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., Kusnitz, J., Debole, M., Esser, S., Delbruck, T., Flickner, M., Modha, D.,  
*[A Low Power, Fully Event-Based Gesture Recognition System](https://doi.org/10.1109/CVPR.2017.781)*,  
 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017. [PDF](http://openaccess.thecvf.com/content_cvpr_2017/papers/Amir_A_Low_Power_CVPR_2017_paper.pdf), [Dataset](#dvsgesture_dataset)
    - [YouTube: IBM Research demonstrates event-based gesture recognition using a brain-inspired chip](https://youtu.be/g08IW-qRomM)
- <a name="Maro20fnins"></a>Maro, J.-M., Ieng, S.-H., Benosman, R.,  
*[Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities](https://doi.org/10.3389/fnins.2020.00275)*,  
Front. Neurosci. (2020), 14:275. [PDF arXiv](https://arxiv.org/pdf/1811.07802)
- <a name="Wang19wacv"></a>Wang, Q., Zhang, Y., Yuan, J., Lu, Y.,  
*[Space-time Event Clouds for Gesture Recognition: from RGB Cameras to Event Cameras](https://cse.buffalo.edu/~jsyuan/papers/2019/WACV_2019_Qinyi.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2019.
- <a name="Ghosh19arxivb"></a>Ghosh, R., Gupta, A., Nakagawa, A., Soares, A. B., Thakor, N. V.,  
*[Spatiotemporal Filtering for Event-Based Action Recognition](https://arxiv.org/pdf/1903.07067)*,  
arXiv:1903.07067, 2019.
- <a name="Wang19cvpr"></a>Wang, Y., Du, B., Shen, Y., Wu, K., Zhao, G., Sun, J., Wen, H.,  
*[EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_EV-Gait_Event-Based_Robust_Gait_Recognition_Using_Dynamic_Vision_Sensors_CVPR_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019.
- <a name="Calabrese19cvprw"></a>Calabrese, E., Taverni, G., Easthope, C., Skriabine, S., Corradi, F., Longinotti, L., Eng, K., Delbruck, T.,  
*[DHP19: Dynamic Vision Sensor 3D Human Pose Dataset](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Calabrese_DHP19_Dynamic_Vision_Sensor_3D_Human_Pose_Dataset_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [Project page](https://sites.google.com/view/dhp19), [Video pitch](https://youtu.be/nFUAQYk3tYA).
- <a name="Pradhan19iscas"></a>Pradhan, B. R., Bethi, Y., Narayanan, S., Chakraborty, A., Thakur, C. S.,  
*[N-HAR: A Neuromorphic Event-Based Human Activity Recognition System Using Memory Surfaces](https://doi.org/10.1109/ISCAS.2019.8702581)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2019.
- <a name="Sokolova19mva"></a>Sokolova, A., Konushin, A.,  
*[Human identification by gait from event-based camera](https://doi.org/10.23919/MVA.2019.8758019)*,  
Int. Conf. Machine Vision Applications (MVA), 2019.
- <a name="Chadha19icassp"></a>Chadha, A., Bi, Y., A., Abbas, A., Andreopoulos, Y.,  
*[Neuromorphic Vision Sensing for CNN-based Action Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8683606),*  
IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), 2019, [Github Page](https://github.com/PIX2NVS/NVS_ActionRecognition).
- <a name="Chen19tci"></a>Chen, H., Liu, W., Goel, R., Lua, R., Siddharth, M., Huang, Y., Veeraraghavan, A., Patel, A.,  
*[Fast Retinomorphic Event-Driven Representations for Video Gameplay and Action Recognition](https://doi.org/10.1109/TCI.2019.2948755)*,  
IEEE Trans. Comput. Imag. (TCI), 6:276-290, 2019.
- <a name="Lungu19jetcas"></a>Lungu, I.-A., Liu, S.-C., Delbruck, T.,  
*[Incremental learning of hand symbols using event-based cameras](https://doi.org/10.1109/JETCAS.2019.2951062)*,  
IEEE J. Emerging Sel. Topics Circuits Syst. (JETCAS), 2019.
    - <a name="Lungu19aicas"></a>Lungu, I. A., Liu, S.-C., Delbruck, T.,  
*[Fast event-driven incremental learning of hand symbols](https://doi.org/10.1109/AICAS.2019.8771472)*,  
IEEE Int. Conf. AI Circuits Syst. (AICAS), 2019.
- [Park et al., IEDM 2019](#Park19iedm),  
*Low-Latency Interactive Sensing for Machine Vision*.
- <a name="Vasudevan20fg"></a>Vasudevan, A., Negri, P., Linares-Barranco, B., Serrano-Gotarredona, T.,  
*[Introduction and Analysis of an Event-Based Sign Language Dataset](https://doi.ieeecomputersociety.org/10.1109/FG47880.2020.00069)*,  
IEEE Int. Conf. Automatic Face and Gesture Recognition (FG), 2020. [Dataset](http://www2.imse-cnm.csic.es/neuromorphs/index.php/SL-ANIMALS-DVS-Database)
- <a name="Chen20iscas"></a>Chen, J., Meng, J., Wang, X., Yuan, J.,  
*[Dynamic Graph CNN for Event-Camera Based Gesture Recognition](https://doi.org/10.1109/ISCAS45731.2020.9181247)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2020.
- <a name="Huang20arXiv"></a>Huang, C.,  
*[Event-based Action Recognition Using Timestamp Image Encoding Network](https://arxiv.org/pdf/2009.13049)*,  
arXiv, 2020.
- <a name="Innocenti20icpr"></a>Innocenti, S. U., Becattini, F., Pernici, F., Del Bimbo, A.,  
*[Temporal Binary Representation for Event-Based Action Recognition](https://arxiv.org/pdf/2010.08946)*,  
Int. Conf. Pattern Recognition (ICPR), 2020.
- <a name="Harrigan20icassp"></a>Harrigan, S., Coleman, S., Kerr, D., Yogarajah, P., Fang, Z., Wu, C.,  
*[Neural Coding Strategies for Event-Based Vision Data](https://doi.org/10.1109/ICASSP40776.2020.9054584)*,  
IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), 2020.
- <a name="Chen20jsen"></a>Chen, G., Hong, L., Dong, J., Liu, P., Conradt, J., Knoll, A.,  
*[EDDD: Event-based drowsiness driving detection through facial motion analysis with neuromorphic vision sensor](https://doi.org/10.1109/JSEN.2020.2973049)*,  
IEEE Sensors Journal, 20(11):6170-6181, 2020.
- <a name="Chen21tfis"></a>Chen, G., Liu, P., Liu, Z., Tang, H., Hong, L., Dong, J., Conradt, J., Knoll, A.,  
*[NeuroAED: Towards Efficient Abnormal Event Detection in Visual Surveillance With Neuromorphic Vision Sensor](https://doi.org/10.1109/TIFS.2020.3023791)*,  
IEEE Trans. Information Forensics and Security (TIFS), 16:923-936, 2021.
- <a name="Liu21ijcai"></a>Liu, Q., Xing, D., Tang, H., Ma, D., Pan, G.,  
*[Event-based Action Recognition Using Motion Information and Spiking Neural Networks](https://doi.org/10.24963/ijcai.2021/240)*,  
International Joint Conferences on Artifical Intelligence (IJCAI), 2021. [PDF](https://www.ijcai.org/proceedings/2021/0240.pdf), [DailyAction-DVS Dataset](https://github.com/qianhuiliu/SNN-action-recognition)  
- <a name="key:Sabater22cvprw"></a>Sabater, A., Montesano, L., Murillo, A.,  
*[Event Transformer. A sparse-aware solution for efficient event data processing](https://openaccess.thecvf.com/content/CVPR2022W/ECV/html/Sabater_Event_Transformer._A_Sparse-Aware_Solution_for_Efficient_Event_Data_Processing_CVPRW_2022_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2022. [PDF](https://arxiv.org/abs/2204.03355), [Supp. Video](https://drive.google.com/file/d/1X4OviJTxTUbi2W0zQYKG3qqtEUf98a0p/view?usp=sharing), [Code](https://github.com/AlbertoSabater/EventTransformer).
  - <a name="Sabater23arxiv"></a>Sabater, A., Montesano, L., Murillo, A.,  
  *[Event Transformer+. A multi-purpose solution for efficient event data processing](https://arxiv.org/abs/2211.12222)*,  
  arXiv, 2022.
- <a name="Messikommer22ral"></a>Messikommer, N., Gehrig, D., Gehrig, M., Scaramuzza, D.,  
*[Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](http://dx.doi.org/10.1109/LRA.2022.3145053)*,  
IEEE Robotics and Automation Letters (RA-L), 2022. [PDF](https://rpg.ifi.uzh.ch/docs/RAL22_Messikommer.pdf), [YouTube](https://youtu.be/fZnBSqni6PY), [code](https://github.com/uzh-rpg/rpg_ev-transfer).  
- <a name="Chen22tcyb"></a>Chen, G., Qu, S., Li, Z., Zhu, H., Dong, J., Liu, M., Conradt, J.,  
*[Neuromorphic Vision-Based Fall Localization in Event Streams With Temporal–Spatial Attention Weighted Network](https://doi.org/10.1109/TCYB.2022.3164882)*,  
IEEE Trans. Cybern., 52(9):9251-9262, 2022.
- <a name="Liu22access"></a>Liu, C., Qi, X., Lam, E. Y., Wong, N.,  
*[Fast Classification and Action Recognition With Event-Based Imaging](https://doi.org/10.1109/ACCESS.2022.3177744)*,  
IEEE Access 10, 55638–55649 (2022).
- <a name="Plizzari22cvpr"></a>Plizzari, C., Planamente, Goletto, G., Cannici, M., Gusso, E., Matteucci, M., Caputo, B.,  
[E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition](https://openaccess.thecvf.com/content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html),  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [Project page](https://github.com/EgocentricVision/N-EPIC-Kitchens)
- <a name="Gao23tpami"></a>Gao, Y., Lu, J., Li, S., Ma, N., Du, S., Li, Y., Dai, Q.,  
[Action Recognition and Benchmark Using Event Cameras](https://ieeexplore.ieee.org/document/10198747),  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2023. [Dataset](https://github.com/lujiaxuan0520/THU-EACT-50)
- <a name="Duarte23jmsy"></a>Duarte, L., Neto, P.,  
*[Classification of primitive manufacturing tasks from filtered event data](https://doi.org/10.1016/j.jmsy.2023.03.001)*,  
Journal of Manufacturing Systems, 2023. [Dataset](https://doi.org/10.5281/zenodo.7625961)
- <a name="Gao24tpami"></a>Gao, Y., Lu, J., Li, S., Li, Y., Du, S.,  
[Hypergraph-Based Multi-View Action Recognition Using Event Cameras](https://ieeexplore.ieee.org/abstract/document/10480584),  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2024. [Dataset](https://github.com/lujiaxuan0520/THU-MV-EACT-50)

<a name="learning-representation-features"></a>
### Representation / Feature Extraction
- Camuñas-Mesa, L., Zamarreño-Ramos, C., Linares-Barranco, A., Acosta-Jiménez, A., Serrano-Gotarredona, T., Linares-Barranco, B.  
*[An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors](https://doi.org/10.1109/JSSC.2011.2167409)*,  
IEEE J. of Solid-State Circuits, 47(2):504-517, 2012.
- <a name="Zhao15tnnls"></a>Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., Tang, H.,  
*[Feedforward Categorization on AER Motion Events using Cortex-like Features in a Spiking Neural Network](https://doi.org/10.1109/TNNLS.2014.2362542)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 26(9):1963-1978, 2015.
- <a name="Akolkar15neco"></a>Akolkar, H., Meyer, C., Clady, X., Marre, O., Bartolozzi, C., Panzeri, S., Benosman, R.,  
*[What Can Neuromorphic Event-Driven Precise Timing Add to Spike-Based Pattern Recognition?](https://doi.org/10.1162/NECO_a_00703)*,  
Neural Computation (2015), 27(3):561–593.
- <a name="Lagorce17tpami"></a>Lagorce, X., Orchard, G., Gallupi, F., Shi, B., Benosman, R.,  
*[HOTS: A Hierarchy Of event-based Time-Surfaces for pattern recognition](https://doi.org/10.1109/TPAMI.2016.2574707)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 39(7):1346-1359, 2017. [PDF](https://www.neuromorphic-vision.com/public/publications/1/publication.pdf)
    - <a name="Tapiador20sensors"></a>Tapiador-Morales, R., Maro, J.-M., Jimenez-Fernandez, A., Jimenez-Moreno, G., Benosman, R., Linares-Barranco, A.,  
    [Event-Based Gesture Recognition through a Hierarchy of Time-Surfaces for FPGA](https://doi.org/10.3390/s20123404),  
    Sensors 2020, 20(12), 3404.
- [Clady et al., FNINS](#Clady17fnins),  
*A Motion-Based Feature for Event-Based Pattern Recognition*.
- <a name="Yousefzadeh17iscas"></a>Yousefzadeh, A., Masquelier, T., Serrano-Gotarredona, T., Linares-Barranco, B.,  
*[Live demonstration: Hardware implementation of convolutional STDP for on-line visual feature learning](https://doi.org/10.1109/ISCAS.2017.8050395)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2017.
- <a name="Sullivan17roman"></a>Sullivan, K., Lawson, W.,  
*[Representing motion information from event-based cameras](https://doi.org/10.1109/ROMAN.2017.8172497)*,  
IEEE Int. Symp. Robot and Human Interactive Comm. (RO-MAN), 2017.
- <a name="Li16neucom"></a>Li, H., Li, G., Ji, X., Shi, L.P.,  
*[Deep representation via convolutional neural network for classification of spatiotemporal event streams](https://doi.org/10.1016/j.neucom.2018.02.019)*,  
Neurocomputing 299, 2018.
- <a name="Sironi18cvpr"></a>Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.,    
*[HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](http://openaccess.thecvf.com/content_cvpr_2018/papers/Sironi_HATS_Histograms_of_CVPR_2018_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018. [PDF](https://arxiv.org/abs/1803.07913).
    - [N-CARS Dataset](#ncars_dataset): A large real-world event-based dataset for car classification.
- <a name="LiuW18arxiv"></a>Liu, W., Chen, H., Goel, R., Huang, Y., Veeraraghavan, A., Patel, A.,  
*[Fast Retinomorphic Event-Driven Representations for Video Recognition and Reinforcement Learning](https://arxiv.org/pdf/1805.06374.pdf)*,  
arXiv: 1805.06374, 2018.
- <a name="Cannici19cvprw"></a>Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.,  
*[Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Cannici_Asynchronous_Convolutional_Networks_for_Object_Detection_in_Neuromorphic_Cameras_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [PDF](https://arxiv.org/pdf/1805.07931). [Video pitch](https://youtu.be/JKRSeg3WrGw)
- <a name="Afshar18fnins"></a>Afshar, S., Hamilton, T. J., Tapson, J., van Schaik, A., Cohen, G.,  
*[Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition](https://doi.org/10.3389/fnins.2018.01047)*,  
Front. Neurosci. (2018), 12:1047.
- <a name="Sekikawa183dv"></a>Sekikawa, Y., Ishikawa, K., Hara, K., Yoshida, Y., Suzuki, K., Sato, I., Saito, H.,  
*[Constant Velocity 3D Convolution](https://doi.org/10.1109/3DV.2018.00047)*,  
IEEE Int. Conf. 3D Vision (3DV), 2018.
    - <a name="Sekikawa18access"></a>Sekikawa, Y., Ishikawa, K., Saito, H.,  
*[Constant Velocity 3D Convolution](https://doi.org/10.1109/ACCESS.2018.2883340)*,  
IEEE Access, 6:76490-76501, 2018.
- <a name="Chen19fnins"></a>Chen, G., Chen, J., Lienen, M., Conradt, J., Roehrbein, F., Knoll, A.C.,  
*[FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition](https://dx.doi.org/10.3389%2Ffnins.2019.00073)*,  
Front. Neurosci. (2019), 13:73.
- <a name="Tapiador18tbiocas"></a>Tapiador-Morales, R., Linares-Barranco, A., Jimenez-Fernandez, A., Jimenez-Moreno, G.  
*[Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA](https://doi.org/10.1109/TBCAS.2018.2880012)*,  
IEEE Trans. Biomed. Circuits Syst, 13(1):159-169, 2019.
- <a name="Ghosh19arxiv"></a>Ghosh, R., Gupta, A., Tang, S., Soares, A. B., Thakor, N. V.,  
*[Spatiotemporal Feature Learning for Event-Based Vision](https://arxiv.org/pdf/1903.06923)*,  
arXiv:1903.06923, 2019.
- <a name="Sekikawa19cvpr"></a>Sekikawa, Y., Hara, K., Saito, H.,  
*[EventNet: Asynchronous Recursive Event Processing](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sekikawa_EventNet_Asynchronous_Recursive_Event_Processing_CVPR_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. [PDF](https://arxiv.org/abs/1812.07045), [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Yusuke_Sekikawa.pdf), [YouTube](https://youtu.be/jHcxQRC-7iQ)
- <a name="Gehrig19iccv"></a>Gehrig, D., Loquercio, A., Derpanis, K. G., Scaramuzza, D.,  
*[End-to-End Learning of Representations for Asynchronous Event-Based Data](http://openaccess.thecvf.com/content_ICCV_2019/papers/Gehrig_End-to-End_Learning_of_Representations_for_Asynchronous_Event-Based_Data_ICCV_2019_paper.pdf)*,  
IEEE Int. Conf. Computer Vision (ICCV), 2019. [PDF](http://rpg.ifi.uzh.ch/docs/ICCV19_Gehrig.pdf), [Suppl. Mat](http://openaccess.thecvf.com/content_ICCV_2019/supplemental/Gehrig_End-to-End_Learning_of_ICCV_2019_supplemental.pdf),  [YouTube](https://youtu.be/bQtSx59GXRY),  [Project Page](https://github.com/uzh-rpg/rpg_event_representation_learning).
- <a name="LinaresBarranco19arxiv"></a>Linares-Barranco, A., Rios-Navarro, A., Tapiador-Morales, R., Delbruck, T.,  
*[Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification](https://arxiv.org/abs/1905.07419)*,  
arXiv:1905.07419, 2019.
- <a name="Afshar19arxiv"></a>Afshar, S., Xu, Y., Tapson, J., van Schaik, A., Cohen, G.,  
*[Event-based Feature Extraction using Adaptive Selection Thresholds](https://arxiv.org/pdf/1907.07853)*,  
arXiv:1907.07853, 2019
- <a name="Zhu19arxiv"></a>Zhu, A.Z., Wang, Z., Daniilidis, K.,  
*[Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform](https://arxiv.org/abs/1902.06820)*,  
arXiv:1902.06820, 2019.
- <a name="Baldwin19iciar"></a>Baldwin R.W., Almatrafi M., Kaufman J.R., Asari V., Hirakawa K.,  
*[Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras](https://link.springer.com/chapter/10.1007/978-3-030-27272-2_35)*,  
Int. Conf. on Image Analysis and Recognition (ICIAR), 2019. [PDF](https://rdcu.be/bQcGk), [Code](https://github.com/bald6354/iets).
- <a name="Bi19arxiv"></a>Bi, Y., Chadha, A., Abbas, A.,  Bourtsoulatze, E., Andreopoulos, Y.,  
*[Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing](https://arxiv.org/pdf/1910.03579),*  
arXiv:1910.03579, 2019.
- <a name="Zhu19arxivGAN"></a>Zhu, A., Wang, Z., Khant, K., Daniilidis, K.,  
*[EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://arxiv.org/pdf/1912.01584)*,  
arXiv:1912.01584, 2019.
- <a name="Cannici20eccv"></a>Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.,  
*[A Differentiable Recurrent Surface for Asynchronous Event-Based Data](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650137.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650137-supp.pdf), [PDF](https://arxiv.org/pdf/2001.03455), [Videos](https://drive.google.com/drive/folders/1KzhKKwJGXvMnhbg1l6gEArgYote7WW8V)
- <a name="Xu20arxiv"></a>Xu, F., Lin, S., Yang, W., Yu, L., Dai, D., Xia, G.,  
*[Matching Neuromorphic Events and Color Images via Adversarial Learning](https://arxiv.org/pdf/2003.00636)*,  
arXiv:2003.00636, 2020.
- <a name="Messikommer2eccv"></a>Messikommer, N., Gehrig, D., Loquercio, A., Scaramuzza, D.,  
*[Event-based Asynchronous Sparse Convolutional Networks](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530409.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530409-supp.zip), [PDF](https://arxiv.org/pdf/2003.09148), [YouTube](https://youtu.be/VD7Beh_-7eU), [Code](https://github.com/uzh-rpg/rpg_asynet)
- <a name="Hu20eccv"></a>Hu, Y., Delbruck, T., Liu, S.-C.,  
*[Learning to Exploit Multiple Vision Modalities by Using Grafted Networks](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610086.pdf)*,  
European Conf. Computer Vision (ECCV), 2020. [Suppl. Mat.](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610086-supp.zip), [PDF](https://arxiv.org/pdf/2003.10959)
- <a name="Samadzadeh20arxiv"></a>Samadzadeh, A., Far, F.S.T., Javadi, A., Nickabadi, A., Chehreghani, M. H.,  
*[Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction](https://arxiv.org/pdf/2003.12346)*,  
arXiv:2003.12346, 2020. [Code](https://github.com/aa-samad/conv_snn)
- <a name="Lin20ral"></a>Lin, S., Xu, F., Wang., X., Yang, W., Yu, L.,  
*[Efficient Spatial-Temporal Normalization of SAE Representation for Event Camera](https://doi.org/10.1109/LRA.2020.2995332)*,  
IEEE Robotics and Automation Letters (RA-L), 5(3):4265-4272, 2020.
- <a name="Kostadinov21icpr"></a>Kostadinov, D., Scaramuzza, D.,  
*[Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation](https://arxiv.org/pdf/2009.11044)*,  
IAPR IEEE Int. Conf. Pattern Recognition (ICPR), Milan, 2021.
- <a name="Wang21tpami"></a>Wang. Y., Zhang, X., Shen, Y., Du, B., Zhao, G., Lizhen, L. C. C., Wen, H.,  
*[Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks](https://doi.org/10.1109/TPAMI.2021.3054886)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 44(7)3436:3449, 2021. [Code](https://github.com/zhangxiann/TPAMI_Gait_Identification).
- <a name="Baldwin22tpami"></a>Baldwin, R., Ruixu, L., Almatrafi, M., Asari, V., Hirakawa, K.,  
*[Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://doi.org/10.1109/TPAMI.2022.3172212)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 45(2)2519:2532, 2022. [PDF](https://arxiv.org/abs/2103.06108), [Code](https://github.com/bald6354/tore_volumes).
- <a name="Chen22tcsvt"></a>Chen, Z., Wu, J., Hou, J., Li, L., Dong, W., Shi, G.,  
*[ECSNet: Spatio-Temporal Feature Learning for Event Camera](https://doi.org/10.1109/TCSVT.2022.3202659)*,  
IEEE Trans. Circuits Syst. Video Technol. (TCSVT), 2022.
- <a name="Elshair23access"></a>El Shair, Z., Hassani, A., Rawashdeh, S.,   
*[CSTR: A Compact Spatio-Temporal Representation for Event-Based Vision](https://doi.org/10.1109/ACCESS.2023.3316143)*,   
IEEE Access, 2023. [Code](https://github.com/Zelshair/cstr-event-vision).

<a name="learning-regression"></a>
### Regression Tasks
- <a name="Maqueda18cvpr"></a>Maqueda, A.I., Loquercio, A., Gallego, G., Garcia, N., Scaramuzza, D.,  
*[Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](http://openaccess.thecvf.com/content_cvpr_2018/papers/Maqueda_Event-Based_Vision_Meets_CVPR_2018_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018. [PDF](http://rpg.ifi.uzh.ch/docs/CVPR18_Maqueda.pdf), [Poster](http://rpg.ifi.uzh.ch/docs/CVPR18_Maqueda_poster.pdf),  [YouTube](https://youtu.be/_r_bsjkJTHA).
- [Zhu et al., RSS 2018](#Zhu18rss),  
*EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras.*
- [Paredes-Valles et al., TPAMI 2019](#ParedesValles19tpami),  
*Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception.*
- [Gallego et al., CVPR 2019](#Gallego19cvpr),  
*Focus Is All You Need: Loss Functions For Event-based Vision*.
- [Rebecq et al., TPAMI 2020](#Rebecq20tpami),  
*High Speed and High Dynamic Range Video with an Event Camera*.
    - [Rebecq et al., CVPR 2019](#Rebecq19cvpr),  
*Events-to-Video: Bringing Modern Computer Vision to Event Cameras*.
- [Zhu et al., CVPR 2019](#Zhu19cvpr),  
*Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion.*
- <a name="Hu19aicas"></a>Hu, Y., Chen, H. M., Delbruck, T.,  
*[Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments](https://doi.org/10.1109/AICAS.2019.8771520)*,  
IEEE Int. Conf. AI Circuits Syst. (AICAS), 2019.
- <a name="Hersche20islped"></a>Hersche, M., Mello Rella, E., Di Mauro, A., Benini, L., Rahimi, A.,  
*[Integrating Event-based Dynamic Vision Sensors with Sparse Hyperdimensional Computing](https://www.research-collection.ethz.ch/handle/20.500.11850/425534)*,  
Int. Symp. Low Power Electronics and Design (ISLPED), 2020. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/425534/HD_DVS_camera_ready.pdf?sequence=1&isAllowed=y)
- [Hidalgo-Carrió et al., 3DV 2020](#Hidalgo20threedv),
*Learning Monocular Dense Depth from Events.*
- [Gehrig et al., RA-L, 2021](#Gehrig21ral)   
*Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction*
- [Hagenaars et al., NeurIPS 2021](#Hagenaars21neurips),  
*Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks.*


<a name="learning-methods-frameworks"></a>
### Learning Methods / Frameworks
- <a name="PerezCarrasco13tpami"></a>Perez-Carrasco, J. A., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., Linares-Barranco, B.,  
*[Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate-Coding and Coincidence Processing. Application to Feed-Forward ConvNets](https://doi.org/10.1109/TPAMI.2013.71)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 35(11):2706-2719, 2013.
- <a name="Stromatias17fnins"></a>Stromatias, E., Soto, M., Serrano-Gotarredona, T., Linares-Barranco, B.,  
*[An Event-Based Classifier for Dynamic Vision Sensor and Synthetic Data](https://doi.org/10.3389/fnins.2017.00350)*,  
Front. Neurosci. (2017), 11:350.
- <a name="Haessig18spie"></a>Haessig, G. and Benosman, R.,  
*[A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors](https://doi.org/10.1117/12.2305933)*,  
Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391U, 2018. [PDF](https://arxiv.org/pdf/1804.09236.pdf)
- <a name="Shrestha18nips"></a>Shrestha, S., Orchard, G.,  
*[SLAYER: Spike Layer Error Reassignment in time](https://arxiv.org/abs/1810.08646)*,  
Advances in Neural Information Processing Systems (NeurIPS) 2018. [PDF](https://arxiv.org/pdf/1810.08646.pdf), [YouTube](https://youtu.be/JGdatqqci5o).
- <a name="Macanovic18arxiv"></a>Macanovic, M., Chersi, F., Rutard, F., Ieng, S.-H., Benosman, R.,  
*[When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning framework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware](https://arxiv.org/pdf/1811.07672)*,  
arXiv: 1811.07672, 2018.
- <a name="Zanardi19rss"></a>Zanardi, A., Aumiller, A.J., Zilly, J., Censi, A., Frazzoli, E.,  
*[Cross-Modal Learning Filters for RGB-Neuromorphic Wormhole Learning](http://www.roboticsproceedings.org/rss15/p45.html)*,  
Robotics: Science and Systems (RSS), 2019. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/349414/1/rsswebsite.pdf)
- <a name="Kaiser19arxiv"></a>Kaiser, J., Friedrich, A., Vasquez Tieck, J.C., Reichard, D., Roennau, A., Neftci, E., Dillmann, R.,  
*[Embodied Neuromorphic Vision with Event-Driven Random Backpropagation](https://arxiv.org/abs/1904.04805)*,  
arXiv, 2019. [PDF](https://arxiv.org/pdf/1904.04805), [Video](https://neurorobotics-files.net/index.php/s/sBQzWFrBPoH9Dx7)
- <a name="Gehrig20cvpr"></a>Gehrig, D., Gehrig, M., Hidalgo-Carrió, J., Scaramuzza, D.,  
*[Video to Events: Recycling Video Datasets for Event Cameras](http://rpg.ifi.uzh.ch/docs/CVPR20_Gehrig.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [YouTube](https://youtu.be/uX6XknBGg0w),  [Project page](https://github.com/uzh-rpg/rpg_vid2e)
- <a name="Deng21tip"></a>Deng, Y., Chen, H., Chen, H. and Li, Y.,  
*[Learning From Images: A Distillation Learning Framework for Event Cameras](https://ieeexplore.ieee.org/abstract/document/9426390)*,  
IEEE Trans. Image Process. (TIP), 30:4919-4931, 2021. [Dataset](https://github.com/SuppDoc/EventKD)
- <a name="Schaefer22cvpr"></a>Schaefer, S., Gehrig, D., Scaramuzza, D.,  
*[AEGNN: Asynchronous Event-based Graph Neural Networks](http://dx.doi.org/10.1109/CVPR52688.2022.01205)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [PDF](https://rpg.ifi.uzh.ch/docs/CVPR22_Schaefer.pdf), [YouTube](https://youtu.be/opbFE6OsAeA), [code](https://github.com/uzh-rpg/aegnn/).  
- <a name="Gehrig22arxiv_graph"></a>Gehrig D., Scaramuzza D.,  
*[Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras](https://arxiv.org/abs/2211.12324)*,  
arXiv, 2022.  
- <a name="Aydin23arxiv"></a>Aydin, A., Gehrig, M., Gehrig, D., Scaramuzza, D.,  
*[A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception](https://arxiv.org/abs/2303.14176)*,  
arXiv, 2023. [PDF](https://rpg.ifi.uzh.ch/docs/arxiv23_aydin.pdf).  
- <a name="Klenk24wacv"></a>Klenk, S., Bonello, D., Koestler, L., Araslanov, N., Cremers, D.,  
*[Masked Event Modeling: Self-Supervised Pretraining for Event Cameras](https://www.doi.org/10.1109/LRA.2023.3240646)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2024. [PDF](https://arxiv.org/pdf/2212.10368), [Code](https://github.com/tum-vision/mem).


<a name="signal_processing"></a>
## Signal Processing
- <a name="Ieng14ieee"></a>Ieng, S.-H., Posch, C., Benosman, R.,  
*[Asynchronous Neuromorphic Event-Driven Image Filtering](https://doi.org/10.1109/JPROC.2014.2347355)*,  
Proc. IEEE, 102(10):1485-1499, 2014. [PDF](http://neuromorphic-vision.com/public/publications/20/publication.pdf)
- <a name="Fillatre15eusipco"></a>Fillatre, L., Antonini, M.,   
*[Uniformly minimum variance unbiased estimation for asynchronous event-based cameras](https://doi.org/10.1109/ICIP.2014.7025834)*,  
IEEE Int. Conf. Image Processing (ICIP), 2014.
- [Mueggler et al., ICRA 2015](#Mueggler15icra),  
*Lifetime Estimation of Events from Dynamic Vision Sensors*.
- <a name="Liu15iscas"></a>Klein, P., Conradt, J., Liu, S.-C.,  
*[Scene stitching with event-driven sensors on a robot head platform](https://doi.org/10.1109/ISCAS.2015.7169173)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
- <a name="Fillatre15eusipco"></a>Fillatre, L.,  
*[Bayes classification for asynchronous event-based cameras](https://doi.org/10.1109/EUSIPCO.2015.7362498)*,  
European Signal Process. Conf. (EUSIPCO), 2015.
- <a name="Sabatier17tip"></a>Sabatier, Q., Ieng, S.-H., Benosman, R.,  
*[Asynchronous Event-Based Fourier Analysis](https://doi.org/10.1109/TIP.2017.2661702)*,  
IEEE Trans. Image Process. (TIP) 26(5):2192-2202, 2017. [PDF](https://www.neuromorphic-vision.com/public/publications/7/publication.pdf), [Suppl. Mat.](https://www.neuromorphic-vision.com/public/publications/7/supplementaryMaterial.zip)
- <a name="Ieng18fnins"></a>Ieng, S.-H., Lehtonen, E., Benosman, R.,  
*[Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals](https://doi.org/10.3389/fnins.2018.00373)*,  
Front. Neurosci. (2018), 12:373.
- [Scheerlinck et al., RAL 2019](#Scheerlinck19ral),  
*Asynchronous Spatial Image Convolutions for Event Cameras*.
- <a name="Lee19arxiv"></a>Lee, S., Kim, H., Kim, H.J.,  
*[Edge Detection for Event Cameras using Intra-pixel-area Events](https://bmvc2019.org/wp-content/uploads/papers/1140-paper.pdf)*,  
British Machine Vision Conf. (BMVC), 2019. [PDF](https://arxiv.org/pdf/1907.07469)
- <a name="Liu19msp"></a>Liu, S.-C., Rueckauer, B., Ceolini, E., Huber, A., Delbruck, T.,  
*[Event-Driven Sensing for Efficient Perception: Vision and audition algorithms](https://doi.org/10.1109/MSP.2019.2928127)*,  
IEEE Signal Process. Mag., 36(6):29-37, Nov. 2019.
- <a name="Sengupta21ciss1"></a>Sengupta, J. P., Villemur, M., Andreou, A. G.,  
*[A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering](https://doi.org/10.1109/CISS50987.2021.9400308)*,  
55th Annual Conf. on Information Sciences and Systems (CISS), 2021, pp. 1-6.


<a name="denoising"></a>
### Event Denoising
- <a name="Delbruck08ff"></a>Delbruck, T.,  
*[Frame-free dynamic digital vision](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.192.2794)*,  
Proc. Int. Symp. on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, 2008, 1:21–26. [PDF](https://drive.google.com/file/d/0BzvXOhBHjRheTS1rSVlZN0l2MDg)
- <a name="Khodamoradi19tetc"></a>Khodamoradi, A., Kastner, R.,  
*[O(N)-Space Spatiotemporal Filter for Reducing Noise in Neuromorphic Vision Sensors](https://doi.org/10.1109/TETC.2017.2788865)*,  
IEEE Trans. Emerging Topics in Computing, 2018.
- <a name="Berthelon18tbcs"></a>Berthelon, X., Chenegros, G., Finateu, T., Ieng, S.H., Benosman, R.,  
*[Effects of Cooling on the SNR and Contrast Detection of a Low-Light Event-Based Camera](https://doi.org/10.1109/TBCAS.2018.2875202)*,  
IEEE Trans. Biomed. Circuits Syst., 12(6):1467-1474, 2018.
- <a name="Baldwin20cvpr"></a>Baldwin R.W., Almatrafi M., Asari V., Hirakawa K.,  
*[Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](http://openaccess.thecvf.com/content_CVPR_2020/html/Baldwin_Event_Probability_Mask_EPM_and_Event_Denoising_Convolutional_Neural_Network_CVPR_2020_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [PDF](https://arxiv.org/abs/2003.08282), [Dataset](https://sites.google.com/a/udayton.edu/issl/software/dataset)
- <a name="Feng20app"></a>Feng, Y., Lv, H., Liu, H., Zhang, Y., Xiao, Y., Han, C.,  
*[Event Density Based Denoising Method for Dynamic Vision Sensor](https://doi.org/10.3390/app10062024)*,  
Appl. Sci. 10(6), 2024, 2020.
- <a name="Wut20mm"></a>Wu, J., Ma, C., Li, L., Dong, W., Shi, G.,  
*[Probabilistic Undirected Graph Based Denoising Method for Dynamic Vision Sensor](https://doi.org/10.1109/TMM.2020.2993957)*,  
 IEEE Trans. Multimedia, 2020.
- <a name="Wu20icassp"></a>Wu, J., Ma, C., Yu, X., Shi, G.,  
*[Denoising of Event-Based Sensors with Spatial-Temporal Correlation](https://doi.org/10.1109/ICASSP40776.2020.9053002)*,  
IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), 2020.
    - <a name="Wu20tmm"></a>Wu, J., Ma, C., Li, L., Dong, W., Shi, G.,  
*[Probabilistic Undirected Graph Based Denoising Method for Dynamic Vision Sensor](https://doi.org/10.1109/TMM.2020.2993957)*,  
IEEE Trans. Multimedia (TMM), 2020.
- <a name="Guo20arxiv"></a>Guo, S., Kang, Z., Wang, L., Zhang, L., Chen, X., Li, S., Xu, W.,  
*[A Noise Filter for Dynamic Vision Sensors based on Global Space and Time Information](https://arxiv.org/pdf/2004.04079)*,  
arXiv, 2020.
- [Baldwin et al., TPAMI 2022](#Baldwin22tpami).  
*Time-Ordered Recent Event (TORE) Volumes for Event Cameras*.  
- <a name="Alkendi22tnnls"></a>Alkendi, Y., Azzam, R., Ayyad, A., Javed, S., Seneviratne, L., Zweiri, Y.,  
*[Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://doi.org/10.1109/TNNLS.2022.3201830)*,  
IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 2022. [PDF](https://arxiv.org/pdf/2112.09685), [Youtube](https://youtu.be/ZM76UaxbuJE), [Dataset](https://github.com/Yusra-alkendi/ED-KoGTL)
- <a name="GuoDelbruck22pami"></a>Guo, S.  and Delbruck, T.,  
[Low Cost and Latency Event Camera Background Activity Denoising](https://doi.org/10.1109/TPAMI.2022.3152999),  
IEEE Trans. Pattern Anal. Mach. Intell., 45(1)785:795, 2022. [Full PDF incl. Supplementary Material](https://drive.google.com/file/d/1HkkEeX4JmIWgaG9y7joxmPgx3lEw5bt6/view?usp=sharing), [DND21 DeNoising Dynamic vision sensors website](https://sites.google.com/view/dnd21/home)
- <a name="Wang22icra"></a>Wang, Z., Yuan, D., Ng, Y., Mahony, R.,   
*[A Linear Comb Filter for Event Flicker Removal](https://doi.org/10.1109/ICRA46639.2022.9812003)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2022. [PDF](https://arxiv.org/pdf/2205.08090.pdf), [Code and Data](https://github.com/ziweiWWANG/EFR), [Youtube](https://www.youtube.com/watch?v=1XgOknDIV00).
- <a name="Zhang23tci"></a>Zhang, P., Ge, Z., Song, L., Lam, E. Y.,   
*[Neuromorphic Imaging with Density-based Spatiotemporal Denoising](https://doi.org/10.1109/TCI.2023.3281202)*,  
IEEE Trans. Comput. Imaging (TCI), 2023. [Code](https://github.com/pz-even/ni-denoising)
<a name="compression"></a>
- [Shiba et al. TPAMI 2024](#Shiba24tpami),  
*Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization*.

### Compression
- <a name="Khan20access"></a>Khan, N., Iqbal, K., Martini, M. G.,  
*[Lossless compression of data from static and mobile Dynamic Vision Sensors - Performance and trade-offs](https://doi.org/10.1109/ACCESS.2020.2996661)*,  
IEEE Access, 2020.
- <a name="Banerjee20arxiv"></a>Banerjee, S., Wang, Z. W., Chopp, H. H., Cossairt, O., Katsaggelos, A.,  
*[Quadtree Driven Lossy Event Compression](https://arxiv.org/pdf/2005.00974.pdf)*,  
arXiv 2020. [PDF](https://arxiv.org/pdf/2005.00974.pdf)
- <a name="Khan21jiot"></a>Khan, N., Iqbal, K., Martini, M. G.,  
*[Time-Aggregation-Based Lossless Video Encoding for Neuromorphic Vision Sensor Data](https://doi.org/10.1109/JIOT.2020.3007866)*,  
IEEE Internet of Things Journal, 2021.
- <a name="Schiopu22lsp"></a>Schiopu, I., Bilcu, R. C.,  
*[Lossless Compression of Event Camera Frames](https://doi.org/10.1109/LSP.2022.3196599)*,  
IEEE Signal Process. Lett. (SPL), 29:1779-1783, 2022.
- <a name="Schiopu22sensors"></a>Schiopu, I., Bilcu, R. C.,  
*[Low-Complexity Lossless Coding of Asynchronous Event Sequences for Low-Power Chip Integration](https://doi.org/10.3390/s222410014)*,  
Sensors, 2022.
- [Freeman et al., MMSys](#Freeman23mmsys), _Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems_


<a name="control"></a>
## Control
- <a name="Delbruck13fnins"></a>Delbruck, T. and Lang, M.,  
*[Robotic Goalie with 3ms Reaction Time at 4% CPU Load Using Event-Based Dynamic Vision Sensor](https://doi.org/10.3389/fnins.2013.00223)*,  
Front. Neurosci. (2013), 7:223. [PDF](http://www.zora.uzh.ch/107801/1/fnins-07-00223.pdf), [YouTube](https://youtu.be/IC5x7ftJ96w)
    - <a name="Delbruck07iscas"></a>Delbruck, T. and Lichtsteiner, P.,  
*[Fast sensory motor control based on event-based hybrid neuromorphic-procedural system](https://doi.org/10.1109/ISCAS.2007.378038)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2007.
    - <a name="Cheng20trxiv"></a>Cheng, R., Nikolic, K.,  
*[System integration of neuromorphic visual (DVS), processing (SpiNNaker) and servomotor modules into an autonomous robot controlled by a Spiking Neural Network](https://www.techrxiv.org/articles/System_integration_of_neuromorphic_visual_DVS_processing_SpiNNaker_and_servomotor_modules_into_an_autonomous_robot_controlled_by_a_Spiking_Neural_Network/11854254)*,  
TechRxiv, 2020. [YouTube](https://www.youtube.com/watch?v=135fH21QXtg)
- <a name="Conradt09iscas"></a>Conradt, J., Cook, M., Berner, R., Lichtsteiner, P., Douglas, R. J., Delbruck, T.,  
*[A Pencil Balancing Robot Using a Pair of AER Dynamic Vision Sensors](https://doi.org/10.1109/ISCAS.2009.5117867)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2009. [PDF](https://www.ini.uzh.ch/~conradt/publications/ISCAS2009-JConradt.pdf), [Poster](https://www.ini.uzh.ch/~conradt/publications/NIPS2008-JConradt.pdf), [Project page](https://www.ini.uzh.ch/~conradt/projects/PencilBalancer/), [YouTube 1](https://youtu.be/XVR5wEYkEGk), [YouTube 2](https://youtu.be/f9UngTdngY4), [YouTube 3](https://youtu.be/yCOnDc5r7p8)
    - <a name="Conradt09iccvw"></a>Conradt, J., Berner, R., Cook, M., Delbruck, T.,  
*[An embedded AER dynamic vision sensor for low-latency pole balancing](https://doi.org/10.1109/ICCVW.2009.5457625)*,  
IEEE Int. Conf. Computer Vision Workshops (ICCVW), 2009. [PDF](http://www.ini.uzh.ch/admin/extras/doc_get.php?id=42580)
- <a name="Mueller15ebccsp"></a>Mueller, E., Censi, A., Frazzoli, E.,  
*[Efficient high speed signal estimation with neuromorphic vision sensors](https://doi.org/10.1109/EBCCSP.2015.7300672)*,  
IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2015.
- <a name="Censi15acc"></a>Censi, A.,  
*[Efficient Neuromorphic Optomotor Heading Regulation](https://doi.org/10.1109/ACC.2015.7171931)*,  
American Control Conference (ACC), 2015.
- <a name="Mueggler15ecmr"></a>Mueggler, E., Baumli, N., Fontana, F., Scaramuzza, D.,  
*[Towards Evasive Maneuvers with Quadrotors using Dynamic Vision Sensors](https://doi.org/10.1109/ECMR.2015.7324048)*,  
Eur. Conf. Mobile Robots (ECMR), Lincoln, 2015. [PDF](http://rpg.ifi.uzh.ch/docs/ECMR15_Mueggler.pdf)
- <a name="Delbruck15iscas"></a>Delbruck, T., Pfeiffer, M., Juston, R., Orchard, G., Mueggler, E., Linares-Barranco, A., Tilden, M. W.,  
*[Human vs. computer slot car racing using an event and frame-based DAVIS vision sensor](https://doi.org/10.1109/ISCAS.2015.7169170)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2015. [YouTube 1](https://youtu.be/CnGPGiZuFRI), [YouTube 2](https://youtu.be/ALneVn-Ls2Q), [YouTube 3](https://youtu.be/AsO1TWS8_VA)
- <a name="Mueller15cdc"></a>Mueller, E., Censi, A., Frazzoli, E.,  
*[Low-latency heading feedback control with neuromorphic vision sensors using efficient approximated incremental inference](https://doi.org/10.1109/CDC.2015.7402002)*,  
IEEE Conf. Decision and Control (CDC), 2015.
- [Moeys et al., EBCCSP 2016](#Moeys16ebccsp).  *VISUALISE Predator/Prey Dataset*.
- <a name="Vasco16humanoids"></a>Vasco, V., Glover, A., Tirupachuri, Y., Solari, F., Chessa M., Bartolozzi C.,  
*[Vergence control with a neuromorphic iCub](https://doi.org/10.1109/HUMANOIDS.2016.7803355)*,  
IEEE Int. Conf. Humanoid Robotics (Humanoids), 2016.
- <a name="Singh16cdc"></a>Singh, P., Yong, S. Z., Gregoire, J., Censi, A., Frazzoli, E.,  
*[Stabilization of linear continuous-time systems using neuromorphic vision sensors](https://doi.org/10.1109/CDC.2016.7798722)*,  
IEEE Conf. Decision and Control (CDC), 2016.
    - <a name="Singh17cdc"></a>Singh, P., Yong, S. Z., Frazzoli, E.,  
*[Stabilization of stochastic linear continuous-time systems using noisy neuromorphic vision sensors](https://doi.org/10.23919/ACC.2017.7963008)*,  
American Control Conference (ACC), 2017.
    - <a name="Singh19tac"></a>Singh, P., Yong, S. Z., Frazzoli, E.,  
*[Regulation of Linear Systems Using Event-Based Detection Sensors](https://doi.org/10.1109/TAC.2018.2876997)*,  
IEEE Trans. Automatic Control, 2019.
- <a name="Blum17rss"></a>Blum, H., Dietmüller, A., Milde, M., Conradt, J., Indiveri, G., Sandamirskaya, Y.,  
*[A neuromorphic controller for a robotic vehicle equipped with a Dynamic Vision Sensor](http://www.roboticsproceedings.org/rss13/p35.pdf)*,  
Robotics: Science and Systems (RSS), 2017.
- <a name="Glover18icra"></a>Glover, A., Vasco, V., Bartolozzi, C.,  
*[A Controlled-Delay Event Camera Framework for On-Line Robotics](https://doi.org/10.1109/ICRA.2018.8460541)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2018.
- <a name="Falanga19ral"></a>Falanga, D., Kim, S., Scaramuzza, D.,  
*[How Fast is Too Fast? The Role of Perception Latency in High-Speed Sense and Avoid](http://rpg.ifi.uzh.ch/docs/RAL19_Falanga.pdf)*,  
IEEE Robotics and Automation Letters (RA-L), 2019. [YouTube](https://youtu.be/sbJAi6SXOQw)
- <a name="Sugimoto20icra"></a>Sugimoto, R., Gehrig, M., Brescianini, D., Scaramuzza, D.,  
*[Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras](https://arxiv.org/pdf/1911.04553)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2020. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA20_Sugimoto.pdf), [YouTube](https://youtu.be/3nIznSMCMtc)
- <a name="Youssef20ral"></a>Youssef, I., Mutlu, M., Bayat, B., Crespi, A., Hauser, S., Conradt, J., Bernardino, A., Ijspeert, A. J.,  
*[A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras](https://doi.org/10.1109/LRA.2020.2972839)*,  
IEEE Robotics and Automation Letters (RA-L), 5(2):2395-2402, April 2020. [PDF](https://infoscience.epfl.ch/record/275495/files/youssef2020.pdf), [YouTube](https://youtu.be/-b6q9FNLWrg).
- <a name="Stagsted20rss"></a>Stagsted, R. K., Vitale, A., Binz, J., Renner, A., Larsen, L. B., Sandamirskaya, Y.,  
*[Towards neuromorphic control: A spiking neural network based PID controller for UAV](http://www.roboticsproceedings.org/rss16/p074.pdf)*,  
Robotics: Science and Systems (RSS), 2020. [PDF](http://www.roboticsproceedings.org/rss16/p074.pdf), [YouTube](https://youtu.be/sdnEosW5NGQ), [Suppl. Video](https://youtu.be/lnGQqz7MM8w)
- <a name="Hagenaars20ral"></a>Hagenaars, J. J., Paredes-Vallés, F., Bohté, S. M., de Croon, G. C. H. E.,  
*[Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs](https://doi.org/10.1109/LRA.2020.3012129)*,  
IEEE Robotics and Automation Letters (RA-L), 5(4):6239-6246, Oct. 2020. [PDF](https://arxiv.org/pdf/2003.03118), [PDF](https://ras.papercept.net/proceedings/IROS20/2654.pdf).
- <a name="Stagsted20iros"></a>Stagsted, R. K., Vitale, A., Renner A., Larsen, L. B., Christensen, A. L., Sandamirskaya, Y.,  
*[Event-Based PID Controller Fully Realized in Neuromorphic Hardware: A One DoF Study](https://ras.papercept.net/proceedings/IROS20/1350.pdf)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2020.
- <a name="Delbruck21cvprw"></a>Delbruck, T., Graca, R., Paluch, M.,  
*[Feedback control of event cameras](https://tub-rip.github.io/eventvision2021/papers/2021CVPRW_Feedback_control_of_event_cameras.pdf)*, IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
- <a name="Vitale21icra"></a>Vitale, A., Renner, A., Nauer, C., Scaramuzza, D., Sandamirskaya, Y.,  
*[Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://doi.org/10.1109/ICRA48506.2021.9560881)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2021. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA21_Vitale.pdf), [YouTube](https://youtu.be/uGEK8eT9Qb0), [PPT Slides](http://rpg.ifi.uzh.ch/docs/ICRA2021_PID%20Controller.pptx).
- <a name="Ayyad21arxiv"></a>Ayyad, A., Halwani, M., Swart, D., Muthusamy, R., Almaskari, F., Zweiri, Y.,   
*[Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems](https://arxiv.org/pdf/2201.01626)*,  
arXiv, 2021. [Video](https://drive.google.com/file/d/1q9QwPvkd7ZcEBcGMIxIVy2r_iRfTKCSe/view).
- <a name="Wang22iros"></a> Wang, Z., Cladera Ojeda, F., Bisulco A., Lee, D., Taylor, C. J., Daniilidis, K., Hsieh, A. M., Lee, D. D., Isler, V.,  
*[EV-Catcher: High-Speed Object Catching Using Low-Latency Event-Based Neural Networks](https://doi.org/10.1109/LRA.2022.3188400)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021.  
<a name="obstacle_avoidance"></a>
- <a name="Ayyad21arxiv"></a>Nair, G.B., Milford, M., Fischer, T.,   
*[Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://arxiv.org/pdf/2403.16425)*,  
arXiv, 2024. [Video](https://youtu.be/8D9gtHqteEQ), [Webpage](https://gokulbnr.github.io/publication/dvs-biasing-vpr).
## Obstacle Avoidance
- <a name="Clady14fnins"></a>Clady, X., Clercq, C., Ieng, S.H., Houseini, F., Randazzo, M., Natale, L., Bartolozzi, C., Benosman, R.,  
*[Asynchronous visual event-based time-to-contact](https://dx.doi.org/10.3389%2Ffnins.2014.00009)*,  
Front. Neurosci. (2014). 8:9. [PDF](https://www.neuromorphic-vision.com/public/publications/30/publication.pdf)
- <a name="Milde15ebccsp"></a>Milde, M. B., Bertrand, O.J.N., Benosman, R., Egelhaaf, M., Chicca, E.,  
*[Bioinspired event-driven collision avoidance algorithm based on optic flow](https://doi.org/10.1109/EBCCSP.2015.7300673)*,  
IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2015 [PDF](https://core.ac.uk/download/pdf/211842521.pdf).
- <a name="Sanket19arxiv"></a>Sanket, N.J., Parameshwara, C.M., Singh, C.D., Kuruttukulam, A.V., Fermüller, C., Scaramuzza, D., Aloimonos, Y.,  
*[EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://arxiv.org/pdf/1906.02919)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2020. [PDF](https://prg.cs.umd.edu/research/EVDodge_files/EVDodge.pdf), [YouTube](https://youtu.be/k1uzsiDI4hM), [Project page](http://prg.cs.umd.edu/EVDodgeNet), [Code](https://github.com/prgumd/EVDodge).
- <a name="Falanga20scirob"></a>Falanga, D., Kleber, K., Scaramuzza, D.,  
*[Dynamic obstacle avoidance for quadrotors with event cameras](https://robotics.sciencemag.org/content/5/40/eaaz9712.full.pdf)*,  
Science Robotics, 5(40):eaaz9712, 2020. [YouTube](https://youtu.be/BzykucxFddI)
- <a name="Yasin20sensors"></a>Yasin, J. N., Mohamed, S. A. S., Haghbayan, M.-H., Heikkonen, J., Tenhunen, H., Yasin, M. M., Plosila, J.,  
*[Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors](https://doi.org/10.1109/SENSORS47125.2020.9278914)*,  
IEEE Sensors 2020. [PDF](https://arxiv.org/pdf/2010.15509).
- <a name="Bisulco21icra"></a>Bisulco, A., Cladera Ojeda, F., Isler, V., Lee, D. D.,  
*[Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors
](https://doi.org/10.1109/ICRA48506.2021.9561290)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2021. [PDF](https://arxiv.org/pdf/2011.09427).
- <a name="walters21iros"></a>Walters, C., Hadfield, S.,  
*[EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance](https://doi.org/10.1109/IROS51168.2021.9636327)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021.
- <a name="he21iros"></a>He, B., Li, H., Wu, S., Wang, D., Zhang, Z., Dong, Q., Xu, C., Gao, F.,  
*[FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing](https://doi.org/10.1109/IROS51168.2021.9636448)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021. [PDF](https://arxiv.org/pdf/2103.05903.pdf), [YouTube](https://www.youtube.com/watch?v=QPpwppeE_x0).
- <a name="Rodriguez22ral"></a>Rodriguez-Gomez, J.P., Tapia, R., Guzman, M.M., Martinez-de Dios, J.R., Ollero, A.,  
*[Free as a Bird: Event-Based Dynamic Sense-and-Avoid for Ornithopter Robot Flight](https://doi.org/10.1109/LRA.2022.3153904)*,  
IEEE Robotics and Automation Letters (RA-L), 2022. [PDF](https://zenodo.org/record/5924071#.YyyGC9JBxhE), [YouTube](https://www.youtube.com/watch?v=cBMcw5jRnfU&list=PL-Kzs2T7Hx3K-IDKsgUwPUnzHmk8Pcsek&index=5).
- <a name="Forrai23icra"></a>Forrai, B., Miki, T., Gehrig, D., Hutter, M., Scaramuzza, D.,  
*[Event-based Agile Object Catching with a Quadrupedal Robot](https://arxiv.org/abs/2303.17479)*,  
IEEE Int. Conf. Robotics and Automation (ICRA), 2023. [PDF](https://rpg.ifi.uzh.ch/docs/ICRA23_Forrai.pdf), [YouTube](https://youtu.be/FpsVB8EO54M), [code](https://github.com/uzh-rpg/event-based_object_catching_anymal/).  


<a name="space"></a>
## Space Applications
- <a name="Cohen17amos"></a>Cohen, G., Afshar, S., van Schaik, A., Wabnitz, A., Bessell, T., Rutten, M., Morreale, B.,  
*[Event-based Sensing for Space Situational Awareness](https://amostech.com/TechnicalPapers/2017/Optical-Systems/Cohen.pdf)*,  
Proc. Advanced Maui Optical and Space Surveillance Technologies Conf. (AMOS), 2017.
- <a name="Cheung18fusion"></a>Cheung, B., Rutten, M., Davey, S., Cohen, G.,  
*[Probabilistic Multi Hypothesis Tracker for an Event Based Sensor](http://dx.doi.org/10.23919/ICIF.2018.8455718)*,  
Int. Conf. Information Fusion (FUSION) 2018, pp. 1-8.
- <a name="Cohen18amos"></a>Cohen, G., Afshar, S., van Schaik, A.,  
*[Approaches for Astrometry using Event-Based Sensors](https://amostech.com/TechnicalPapers/2018/Poster/Cohen.pdf)*,  
Proc. Advanced Maui Optical and Space Surveillance Technologies Conf. (AMOS), 2018.
- <a name="Chin19cvprw"></a>Chin, T.-J., Bagchi, S., Eriksson, A., van Schaik, A.,  
*[Star Tracking using an Event Camera](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Chin_Star_Tracking_Using_an_Event_Camera_CVPRW_2019_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [PDF](https://arxiv.org/pdf/1812.02895). [Project page](https://cs.adelaide.edu.au/~tjchin/startracking/). [Video pitch](https://youtu.be/XuYYhyS8IwM)
- Western Sydney University ICNS
    - [Astrosite](https://www.westernsydney.edu.au/icns/astrosite): [University News](https://www.westernsydney.edu.au/newscentre/news_centre/more_news_stories/world-first_technology_to_revolutionise_space_imaging), [ABC News](https://www.abc.net.au/news/2019-02-24/space-camera-astrosite-created-in-sydney-a-game-change-raaf-says/10842220).
    - [Simultaneous Sky Mapping and Satellite Tracking](https://www.westernsydney.edu.au/icns/research/research_streams/perception/sky_mapping_satellite_tracking)
    - [G. Cohen - The Astrosite mobile observatory - 2020 Telluride Neuromorphic](https://youtu.be/Xnl6x_xvucs)
- <a name="Zolnowski19neosst"></a>Zolnowski, M., Reszelewski, R., Moeys, D.P., Delbruck, T., Kaminski, K.,  
*[Observational Evaluation of Event Cameras Performance in Optical Space Surveillance](https://conference.sdo.esoc.esa.int/proceedings/neosst1/paper/475/NEOSST1-paper475.pdf)*,  
Proc. NEO and Debris Detection Conference, Darmstadt, Germany, Jan. 2019.
- <a name="Bagchi20wacv"></a>Bagchi, S., Chin, T.-J.,  
*[Event-based Star Tracking via Multiresolution Progressive Hough Transforms](http://openaccess.thecvf.com/content_WACV_2020/papers/Bagchi_Event-based_Star_Tracking_via_Multiresolution_Progressive_Hough_Transforms_WACV_2020_paper.pdf)*,  
IEEE Winter Conf. Applications of Computer Vision (WACV), 2020. [PDF](https://arxiv.org/pdf/1906.07866.pdf)
- <a name="Afshar19arxiv"></a>Afshar, S., Nicholson, A. P., van Schaik, A., Cohen, G.,  
*[Event-based Object Detection and Tracking for Space Situational Awareness](https://arxiv.org/pdf/1911.08730)*,  
arXiv:1911.08730, 2019. [Dataset](https://www.westernsydney.edu.au/icns/reproducible_research/publication_support_materials/space_imaging)
- <a name="Ralph19amos"></a>Ralph, N.O., Maybour, D., Bethi, Y., Cohen, G.,  
*[Observations and Design of a new Neuromorphic Event-based All-Sky and Fixed Region Imaging System](https://amostech.com/TechnicalPapers/2019/Optical-Systems-&-Instrumentation/Ralph.pdf)*, 
Proc. Advanced Maui Optical and Space Surveillance Technologies Conf. (AMOS), 2019.
- <a name="Roffe21arxiv"></a>Roffe, S., Akolkar, H., George, A. D., Linares-barranco, B., Benosman, R.,  
*[Neutron-Induced, Single-Event Effects on Neuromorphic Event-based Vision Sensor: A First Step Towards Space Applications](https://arxiv.org/abs/2102.00112)*,  
arXiv, 2021.
- <a name="McMahon21osa"></a>McMahon-Crabtree, P., Monet, D.,  
*[Evaluation of Commercial-off-the-Shelf EventBased Cameras for Space Surveillance Applications](https://www.osapublishing.org/ao/upcoming_pdf.cfm?id=425623)*,  
Applied Optics, 2021.
- <a name="Ralph22fnins"></a>Ralph, N.O., Joubert, D., Jolley, A., Afshar, S., Tothill, N., van Schaik, A. and Cohen, G.,  
*[Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness](https://doi.org/10.3389%2Ffnins.2022.821157)*, 
Frontiers in Neuroscience, 2022.
- <a name="Ralph22arxiv"></a>Ralph, N.O., Marcireau, A., Afshar, S., Tothill, N., van Schaik, A. and Cohen, G.  
*[Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging](https://arxiv.org/abs/2211.09939)*, 
arXiv preprint arXiv:2211.09939, 2022. 
[Dataset](https://github.com/NicRalph213/ICNS_NORALPH_Event_Based-Space_Imaging-Speed_Dataset).
- [Mahlknecht et al., RAL 2022](#Mahlknecht22ral),  
*Exploring Event Camera-based Odometry for Planetary Robots*.



<a name="tactile_sensing"></a>
## Tactile Sensing Applications
- <a name="Rigi18sensors"></a>Rigi, A., Baghaei Naeini, F., Makris, D., Zweiri, Y.,  
*[A Novel Event-Based Incipient Slip Detection Using Dynamic Active-Pixel Vision Sensor (DAVIS)](https://doi.org/10.3390/s18020333)*,  
Sensors 2018, 18, 333.
- <a name="Naeini19tim"></a>Naeini, F. B., Alali, A., Al-Husari, R., Rigi, A., AlSharman, M. K., Makris, D., Zweiri, Y.,  
*[A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications](https://doi.org/10.1109/TIM.2019.2919354)*,  
IEEE Trans. Instrum. Meas., 2019.
- <a name="Muthusamy20access"></a>Muthusamy, R., Huang, X., Zweiri, Y., Seneviratne, L., Gan, D.,  
*[Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation](https://doi.org/10.1109/ACCESS.2020.3017738)*,  
IEEE Access, 2020. [PDF](https://arxiv.org/pdf/2004.07386)
- <a name="Haessig20fnins"></a>Haessig, G., Milde, M.B., Aceituno, P.V., Oubari, O., Knight, J.C., van Schaik, A., Benosman,  R. B., Indiveri, G.,  
*[Event-Based Computation for Touch Localization Based on Precise Spike Timing](https://doi.org/10.3389/fnins.2020.00420)*,  
Front. Neurosci. (2020), 14:420.
- <a name="Naeini20sensors"></a>Naeini, F.B., Makris, D., Dongming, G., Zweiri, Y.,  
*[Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks](https://doi.org/10.3390/s20164469)*,  
Sensors 2020, 20, 16.
- <a name="Taunyazov20rss"></a>Taunyazov, T., Sng, W., Lim, B., Hian, H., Kuan, J., Fatir, A., Tee, B., Soh, H.,  
*[Event-Driven Visual-Tactile Sensing and Learning for Robots](https://roboticsconference.org/program/papers/20/)*,  
Robotics: Science and Systems (RSS), 2020. [PDF](http://www.roboticsproceedings.org/rss16/p020.pdf), [YouTube](https://youtu.be/zPlrqtjEcUY), [Project Page](https://clear-nus.github.io/visuotactile/)
- <a name="WardCherrier20iros"></a>Ward-Cherrier, B., Conradt, J., Catalano, M. G., Bianchi, M., Lepora, N.F.,  
*[A Miniaturised Neuromorphic Tactile Sensor Integrated with an Anthropomorphic Robot Hand](https://ras.papercept.net/proceedings/IROS20/1490.pdf)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2020.


<a name="object_pose_estimation"></a>
## Object Pose Estimation

<a name="human_pose_estimation"></a>
### Human Pose Estimation
- [Calabrese et al., CVPRW 2019](#Calabrese19cvprw),  
*DHP19: Dynamic Vision Sensor 3D Human Pose Dataset*.
- [Zhu et al., arXiv 2019](#Zhu19arxivGAN),  
*EventGAN: Leveraging Large Scale Image Datasets for Event Cameras*.
- [Xu et al., CVPR 2020](#Xu20cvpr),  
*EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera*.
- [Baldwin et al., TPAMI 2022](#Baldwin22tpami),  
*Time-Ordered Recent Event (TORE) Volumes for Event Cameras*.
- <a name="Scarpellini21cvprw"></a>Scarpellini, G., Morerio, P., Del Bue, A.,  
*[Lifting Monocular Events to 3D Human Poses](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Scarpellini_Lifting_Monocular_Events_to_3D_Human_Poses_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](https://iit-pavis.github.io/lifting_events_to_3d_hpe/), [YouTube](https://youtu.be/nwWh3MKdOEM), [Code](https://github.com/IIT-PAVIS/lifting_events_to_3d_hpe).
- <a name="Chen22threedv"></a>Chen, J., Shi, H., Ye, Y., Yang, K., Sun, L., Wang, K.,  
*[Efficient Human Pose Estimation via 3D Event Point Cloud](https://arxiv.org/pdf/2206.04511)*,  
IEEE Int. Conf. 3D Vision (3DV), 2022. [Project page](https://bestchen97.github.io/EventHumanPerception/), [Code](https://github.com/MasterHow/EventPointPose)
- <a name="Zhang23neurocomp"></a> Zhang, Z., Chai, K., Yu, H., Majaj, R., Walsh, F., Wang, E., Mahbub, U., Siegelmann, H., Kim, D., Rahman, T.,  
*[Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment](https://www.sciencedirect.com/science/article/pii/S0925231223005118)*,   
Neurocomputing, 547, 2023. [Code](https://github.com/miracleyoo/yelan-dynamic-dvs-hpe)

<a name="hand_pose_estimation"></a>
### Hand Pose Estimation
- <a name="Naeini20sensors"></a>Rudnev, V., Golyanik, V., Wang, J., Seidel, H.-P., Mueller, F., Elgharib, M., Theobalt, C.,  
*[EventHands: Real-Time Neural 3D Hand Reconstruction from an Event Stream](https://arxiv.org/pdf/2012.06475)*,  
arXiv, 2020. [Project page](https://gvv.mpi-inf.mpg.de/projects/EventHands/)
- <a name="Duarte21sensrev"></a>Duarte, L., Safeea, M., Neto, P.,  
*[Event-based tracking of human hands](https://doi.org/10.1108/SR-03-2021-0095)*,  
Sensor Review, 2021. [Dataset](https://doi.org/10.5281/zenodo.4918319)
- <a name="Xue22bmvc"></a>Xue, Y., Li, H., Leutenegger, S., Stueckler, J.,  
*[Event-based Non-Rigid Reconstruction from Contours](https://bmvc2022.mpi-inf.mpg.de/0078.pdf)*,  
British Machine Vision Conf. (BMVC), 2022. [Project page](https://bmvc2022.mpi-inf.mpg.de/78/)
- <a name="lan23arxiv"></a>Lan, C.,Yin Z.,Basu A.,Chan R.,  
*[Tracking Fast by Learning Slow: An Event-based Speed Adaptive Hand Tracker Leveraging Knowledge in RGB Domain](https://arxiv.org/pdf/2302.14430)*,  
arXiv, 2023. 

<a name="indoor_lighting_estimation"></a>
## Indoor Lighting Estimation
- <a name="Chen21cvpr"></a>Chen, Z., Zheng, Q., Niu, P., Tang, H., Pan, G.,  
*[Indoor Lighting Estimation Using an Event Camera ](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. [Suppl.](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Indoor_Lighting_Estimation_CVPR_2021_supplemental.pdf)


<a name="data_encription"></a>
## Data Encryption
- <a name="Du21aensors"></a>Du, B., Li, W., Wang, Z., Xu, M., Gao, T., Li, J., Wen, H.,  
*[Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation](https://doi.org/10.3390/s21134320)*,  
Sensors, 2021.
- <a name="Zhang24nce"></a>Zhang, P., Zhu, S., Lam, E. Y.,   
*[Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging](https://doi.org/10.1088/2634-4386/ad207b)*,  
Neuromorphic Computing and Engineering, 2024. [PDF](https://arxiv.org/pdf/2306.03369.pdf)

<a name="nuclear_verification"></a>
## Nuclear Verification
- <a name="Glaser22casa"></a>A. Glaser,  
*[Keeping Secrets at a Distance: New Approaches to Nuclear Monitoring and Verification](https://sgs.princeton.edu/sites/default/files/2022-06/ALX-2022-CASA-REV3.pdf)*,  
Distinguished Lecture, Cyber Security in the Age of Large-Scale Adversaries (CASA), Ruhr-Universität Bochum, Germany, 2022.

<a name="optical_communication"></a>
## Optical Communication
- <a name="Wang22iros"></a>Wang, Z., Ng, Y., Henderson, J., Mahony., R.,   
*[Smart Visual Beacons with Asynchronous Optical Communications using Event Cameras](https://arxiv.org/abs/2208.01710)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2022.
[PDF](https://arxiv.org/pdf/2208.01710.pdf), [Code and Data](https://github.com/ziweiWWANG/Event-Beacon-Communication).


<a name="animal_monitoring"></a>
## Animal Behavior Monitoring
- <a name="Hamann22icprw"></a>Hamann, F., Gallego, G.,   
*[Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras](https://arxiv.org/pdf/2207.07332)*,  
26th Int. Conf. Pattern Recognition (ICPR), Visual observation and analysis of Vertebrate And Insect Behavior (VAIB) Workshop, Montreal, Canada, 2022.

<a name="optical_applications"></a>
## Optical Applications
<a name="auto_focus"></a>
### Auto-focus
- <a name="Lin22cvpr"></a>Lin, S., Zhang, Y., Zhang, L., Zhou, B., Luo, X., Pan, J.,  
*[Autofocus for Event Cameras](https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.pdf), [Suppl.](https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Autofocus_for_Event_CVPR_2022_supplemental.zip).
- <a name="Ge23ole"></a>Ge, Z., Wei, H., Xu, F., Gao, Y., Chu, Z., So, H., Lam, E.,  
*[Millisecond autofocusing microscopy using neuromorphic event sensing](https://doi.org/10.1016/j.optlaseng.2022.107247)*,  
Optics and Lasers in Engineering, 2023.
- [Bao et al., Arxiv 2023](#Bao23arxiv),  
*Improving Fast Auto-Focus with Event Polarity*.
- <a name="Lou23cvpr"></a>Lou, H., Teng, M., Yang, Y., Shi, B.,  
*[All-in-Focus Imaging From Event Focal Stack](https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. [PDF](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjU-oPEn7P_AhUVBt4KHW68DiQQFnoECA8QAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FCVPR2023%2Fpapers%2FLou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.pdf&usg=AOvVaw2SxXp7z29FqyHFlGpNosXx)
- <a name="Bao23oe"></a>Bao, Y., Sun, L., Ma, Y., Gu, D. & Wang, K.,  
*[Improving Fast Auto-Focus with Event Polarity](https://doi.org/10.1364/OE.489717)*,  
Opt. Express, 2023. 

<a name="speckle_analysis"></a>
### Speckle Analysis
- <a name="Ge21ao"></a>Ge, Z., Meng, N., Song, L, Lam, E.,  
*[Dynamic laser speckle analysis using the event sensor](https://doi.org/10.1364/AO.412601)*,  
Applied Optics, 2021.
- <a name="Ge21ol"></a>Ge, Z., Gao, Y., So, H., Lam, E.,  
*[Event-based laser speckle correlation for micro motion estimation](https://doi.org/10.1364/OL.430419)*,  
Optics Letters, 2021.
- <a name="Ge22oe"></a>Ge, Z., Zhang, P., Gao, Y., So, H, Lam, E.,  
*[Lens-free motion analysis via neuromorphic laser speckle imaging](https://doi.org/10.1364/OE.444948)*,  
Optics Express, 2022.

<a name="interferometry_or_holography"></a>
### Interferometry or Holography
- <a name="Schober21ol"></a>Schober, C., Pruss, C., Faulhaber, A., Herkommer, A.,  
*[Event based coherence scanning interferometry](https://doi.org/10.1364/OL.437489)*,  
Optics Letters, 2021.

<a name="wavefront_sensing"></a>
### Wavefront sensing
- <a name="Kong20oe"></a>Kong, F., Lambert, A., Joubert, D., Cohen, G.,  
*[Shack-Hartmann wavefront sensing using spatial-temporal data from an event-based image sensor](https://doi.org/10.1364/OE.409682)*,  
Optics Express, 2020.

<a name="super_resolution_imaging"></a>
### Optical super-resolution
- <a name="Cabriel22bioarxiv"></a>Cabriel, C., Specht, C. G. & Izeddin, I.,  
*[Event-based vision sensor enables fast and dense single-molecule localization microscopy](https://doi.org/10.1101/2022.07.22.501162)*,  
bioArxiv, 2022.
- <a name="Mangalwedhekar23natnano"></a>Mangalwedhekar, R., Singh, N., Thakur, C. S., Seelamantula, C. S., Jose, M., and Nair, D.,  
*[Achieving nanoscale precision using neuromorphic localization microscopy](https://doi.org/10.1101/2022.07.22.501162)*,  
Nat. Nanotechnol., 2023.

<a name="schlieren_imaging"></a>
### Schlieren imaging
- <a name="Shiba23tpami"></a>Shiba, S., Hamann, F., Aoki, Y., Gallego, G.,  
*[Event-based Background-Oriented Schlieren](https://doi.org/10.1109/TPAMI.2023.3328188)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2023. [Project page](https://github.com/tub-rip/event_based_bos), [Video](https://youtu.be/Ev52n8KgxIU), [PDF](https://arxiv.org/pdf/2311.00434)

<a name="driver_monitoring_system"></a>
## Driver Monitoring System
<a name="DMS"></a>
### Multi-tasking networks: Face, Head-Pose and Eye-gaze Estimation
- <a name="Ryan23access"></a>Ryan, C., Elrasad, A., Shariff, W., Lemley, J., Kielty, P., Hurney, P. and Corcoran, P.,  
*[Real-Time Multi-Task Facial Analytics With Event Cameras](https://doi.org/10.1109/ACCESS.2023.3297500)*,  
IEEE Access, vol. 11, pp. 76964-76976, 2023.

<a name="Drwosiness_or_yawn"></a>
### Driver Drowsiness or Yawn
- <a name="Chen20jsen"></a>Chen, G., Hong, L., Dong, J., Liu, P., Conradt, J. and Knoll, A.,  
  *[EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor](https://doi.org/10.1109/JSEN.2020.2973049)*,  
IEEE Sensors Journal, vol. 20, no. 11, pp. 6170-6181, 1 June1, 2020.
- <a name="Keilty23access"></a>Kielty, P., Dilmaghani, M.S., Shariff, W., Ryan, C., Lemley, J. and Corcoran, P.,  
 *[Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera](https://doi.org/10.1109/ACCESS.2023.3312190)*,  
IEEE Access, vol. 11, pp. 96363-96373, 2023.

<a name="distraction_detecton"></a>
### Driver Distraction
- <a name="Yang22mfi"></a>Yang, C., Liu, P., Chen, G., Liu, Z., Wu, Y. and Knoll, A.,  
  *[Event-based Driver Distraction Detection and Action Recognition](https://doi.org/10.1109/MFI55806.2022.9913871)*,  
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Bedford, United Kingdom, pp. 1-7, 2022.
- <a name="Shariff23ojvt"></a>Shariff, W., Dilmaghani, M.S., Kielty, P., Lemley, J., Farooq, M.A., Khan, F. and Corcoran, P.,  
  *[Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection](https://doi.org/10.1109/OJVT.2023.3325656)*,  
  IEEE Open Journal of Vehicular Technology, vol. 4, pp. 836-848, 2023.

<a name="face-alignment-landmarking"></a>
## Face Alignment and Landmark Detection
- <a name="Savran20sensors"></a>Savran, A., Bartolozzi, C.,  
*[Face pose alignment with event cameras](https://doi.org/10.3390/s20247079)*,  
Sensors, 20:24, Article 7079, 2020.
- <a name="Savran23cviu"></a>Savran, A.,  
*[Multi-timescale boosting for efficient and improved event camera face pose alignment](https://doi.org/10.1016/j.cviu.2023.103817)*,  
Computer Vision and Image Understanding, Vol. 236, 103817, November 2023.

<a name="voice-activity-detection"></a>
## Visual Voice Activity Detection
- <a name="Savran18fg"></a>Savran, A., Tavarone, R., Higy, B., Badino, L., Bartolozzi, C.,  
*[Energy and computation efficient audio-visual voice activity detection driven by event-cameras](https://doi.org/10.1109/FG.2018.00055)*,  
IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG), 2018.
- <a name="Savran23asyu"></a>Savran, A.,  
*[Fully convolutional event-camera voice activity detection based on event intensity'](https://doi.org/10.1109/ASYU58738.2023.10296754)*,  
IEEE Innovations Intell. Syst. Appl. Conf. (ASYU), 2023.

<a name="simulators-emulators"></a>
# Simulators and Emulators
- <a name="Katz12iscas"></a>Katz, M. L., Nikolic, K., Delbruck, T. (2012),  
*[Live demonstration: Behavioural emulation of event-based vision sensors](https://doi.org/10.1109/ISCAS.2012.6272143)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2012. [PDF](https://drive.google.com/open?id=1asyMIQIAtsl3_KqgfPuZagr_gjKZhbYm)
- <a name="Kaiser16simpar"></a>Kaiser, J., Tieck, J. C. V., Hubschneider, C., Wolf, P., Weber, M., Hoff, M., Friedrich., A., Wojtasik, K., Roennau, A., Kohlhaas, R., Dillmann, R., Zoellner, M. (2016),  
*[Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks](https://doi.org/10.1109/SIMPAR.2016.7862386)*,  
IEEE Int. Conf. on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2016. [PDF](https://www.researchgate.net/profile/Jacques_Kaiser/publication/309558315_Towards_a_framework_for_end-to-end_control_of_a_simulated_vehicle_with_spiking_neural_networks/links/5bb568b192851ca9ed379dc3/Towards-a-framework-for-end-to-end-control-of-a-simulated-vehicle-with-spiking-neural-networks.pdf), [Gazebo DVS plugin](https://github.com/HBPNeurorobotics/gazebo_dvs_plugin)
- <a name="Pineda16ssci"></a>Pineda García, G., Camilleri, P., Liu, Q., Furber, S.,  
*[pyDVS: An extensible, real-time Dynamic Vision Sensor emulator using off-the-shelf hardware](https://doi.org/10.1109/SSCI.2016.7850249)*,  
IEEE Int. Symp. Series on Computational Intelligence (SSCI), 2016. [Code](https://github.com/chanokin/pyDVS)
- [Mueggler et al., IJRR 2017](#Mueggler17ijrr).  
*The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.*
- <a name="Bi17icip"></a>Bi, Y. and Andreopoulos, Y.,  
*[PIX2NVS: Parameterized conversion of pixel-domain video frames to neuromorphic vision streams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8296630),*  
IEEE Int. Conf. Image Processing (ICIP), 2017, [GitHub Page](https://github.com/PIX2NVS/PIX2NVS).
- <a name="Li18bmvc"></a>Li, W., Saeedi, S., McCormac, J., Clark, R., Tzoumanikas, D., Ye, Q., Huang, Y., Tang, R., Leutenegger, S.,  
[Interiornet: Mega-scale multi-sensor photo-realistic indoor scenes dataset](https://arxiv.org/pdf/1809.00716.pdf),  
British Machine Vis. Conf. (BMVC), 2018. [YouTube](https://youtu.be/z8uJh_xUq7A), [Project Page](https://interiornet.org/).
- <a name="Rebecq18corl"></a>Rebecq, H., Gehrig, D., Scaramuzza, D.,  
*[ESIM: an Open Event Camera Simulator](http://rpg.ifi.uzh.ch/docs/CORL18_Rebecq.pdf),*  
Conf. on Robot Learning (CoRL), 2018. [PDF](http://rpg.ifi.uzh.ch/docs/CORL18_Rebecq.pdf), [YouTube](https://youtu.be/ytKOIX_2clo), [Project Page](http://rpg.ifi.uzh.ch/esim/index.html).
- [Gehrig et al. CVPR 2020](#Gehrig20cvpr),  
*Video to Events: Recycling Video Datasets for Event Cameras*.
- <a name="Hu21cvprw"></a>Hu, Y., S.-C., Liu, Delbruck, T.,  
*[v2e: From Video Frames to Realistic DVS Events](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Hu_v2e_From_Video_Frames_to_Realistic_DVS_Events_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](https://sites.google.com/view/video2events/home), [YouTube](https://youtu.be/VzQ7b5-pLag), [Suppl.](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/supplemental/Hu_v2e_From_Video_CVPRW_2021_supplemental.zip), [Code](https://github.com/SensorsINI/v2e).
- <a name="Nehvi21cvprw"></a>Nehvi, J., Golyanik, V., Mueller, F., Seidel, H.-P., Elgharib, M., Theobalt, C.,  
*[Differentiable Event Stream Simulator for Non-Rigid 3D Tracking](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Nehvi_Differentiable_Event_Stream_Simulator_for_Non-Rigid_3D_Tracking_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](http://gvv.mpi-inf.mpg.de/projects/Event-based_Non-rigid_3D_Tracking/), [YouTube](https://youtu.be/dU2zv_Ggp7Y), [Suppl.](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/supplemental/Nehvi_Differentiable_Event_Stream_CVPRW_2021_supplemental.pdf).
- <a name="Joubert21fins"></a>Joubert, C., Marcireau, A., Ralph, N., Jolley, A., van Schaik, A., Cohen, G.,  
[Event Camera Simulator Improvements via Characterized Parameters](https://www.frontiersin.org/articles/10.3389/fnins.2021.702765/full)  
  Front. Neurosci., 2021, [IEBCS simulator](https://github.com/neuromorphicsystems/IEBCS)
- <a name="Gu24mm"></a>Gu, D., Li, J., Zhang, Y., Tian, Y.,   
*[How to Learn a Domain-Adaptive Event Simulator](https://doi.org/10.1145/3474085.3475229)*,  
ACM Int. Conf. on Multimedia (MM), 2021. [PDF](https://cvteam.buaa.edu.cn/papers/2021-MM-Daxin.pdf), [Code](https://github.com/iCVTEAM/LETGAN).
- <a name="Lin22eccv"></a>Lin, S., Ma, Y., Guo, Z., Wen, B.,   
[DVS-Voltmeter: Stochastic process-based event simulator for dynamic vision sensors](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/267_ECCV_2022_paper.php)  
  European Conf. Computer Vision (ECCV), 2022, [PDF](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670571.pdf), [Suppl.](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670571-supp.pdf), [Code](https://github.com/Lynn0306/DVS-Voltmeter).
- <a name="Zhang24icra"></a>Zhang, Z., Cui, S., Chai, K., Yu, H., Dasgupta, S., Mahbub, U., Rahman, T.,   
[V2CE: Video to Continuous Events Simulator](https://arxiv.org/abs/2309.08891)   
  IEEE Int. Conf. Robotics and Automation (ICRA), 2024. [Code](https://github.com/ucsd-hdsi-dvs/V2CE-Toolbox).
- <a name="Gu24tpami"></a>Gu, D., Li, J., Zhu, L., Zhang, Y., Jimmy, S.R.,   
*[Reliable Event Generation With Invertible Conditional Normalizing Flow](http://dx.doi.org/10.1109/TPAMI.2023.3326538)*,  
IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2024. [PDF](https://cvteam.buaa.edu.cn/papers/2024-TPAMI-Daxin.pdf).

<a name="datasets"></a>
# Datasets (sorted by topic)
- [Datasets from the Sensors group at INI](http://sensors.ini.ch/datasets) (Institute of Neuroinformatics), Zurich:
    - [DVS09](https://docs.google.com/document/d/16b4H78f4vG_QvYDK2Tq0sNBA-y7UFnRbNnsGbD1jJOg/edit?usp=sharing) - 	DVS128 Dynamic Vision Sensor Silicon Retina
    - [DVSFLOW16](https://docs.google.com/document/d/1r9sRYANGuDTUcfSSq-sL4sd79SfjHGNRul_10uztDaI/edit?usp=sharing) - 	DVS/DAVIS Optical Flow Dataset
    - [DVSACT16](https://docs.google.com/document/d/1m4gAHPkPIzVvhHirtSjzUJrKiD5uU2I76hTqXhk8CxI/edit?usp=sharing) -	DVS Datasets for Object Tracking, Action Recognition and Object Recognition
    - [PRED18](https://docs.google.com/document/d/e/2PACX-1vQ8HzlVv1ZzUEIfCUfUOUazXi__cacsCMU3LTqECrZk3-8nlyDCe2V29CHh20-cr42j8DrrMkFZGd14/pub) - 	VISUALISE Predator/Prey Dataset
    - [DDD17](https://docs.google.com/document/d/1HM0CSmjO8nOpUeTvmPjopcBcVCk7KXvLUuiZFS6TWSg/pub) - 	DAVIS Driving Dataset 2017
    - [ROSHAMBO17](https://docs.google.com/document/d/1rOltN_BaOTAMbP1chzFZxPjN24eTdbzuCrCM4S2o6qA/edit?usp=sharing) - 	RoShamBo Rock Scissors Paper game DVS dataset
    - [DHP19](https://sites.google.com/view/dhp19/home) - 	DAVIS Human Pose Estimation and Action Recognition
    - [DDD20](https://sites.google.com/view/davis-driving-dataset-2020/home) - End-to-End Event Camera Driving Dataset
    - [DND21](https://sites.google.com/view/dnd21) - DeNoising Dynamic vision sensors dataset
    - [EDFLOW21](https://sites.google.com/view/edflow21/home) - Event Driven Flow dataset
    - [MVSEC-NIGHT21](https://github.com/SensorsINI/MVSEC-NIGHTL21) - MVSEC Nighttime Driving Labeled Cars
    - [DVSD22](https://sites.google.com/view/dvs-disdrometer/home) - Dynamic Vision Sensor Disdrometer
    - [DAVIS24](https://sites.google.com/view/davis24-davis-sample-data/home) - DAVIS Event Camera Sample Data

## Human Pose Estimation
- [Calabrese et al., CVPRW 2019](#Calabrese19cvprw),  
*DHP19: Dynamic Vision Sensor 3D Human Pose Dataset*.
- [Zhang et al., Neurocomputing 2023](#Zhang23neurocomp),  
*Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment*.

## Stereo Depth Estimation
- [Andreopoulos et al., CVPR 2018](#Andreopoulos18cvpr), *A Low Power, High Throughput, Fully Event-Based Stereo System*.
- [Zhu et al., RAL 2018](#Zhu18mvsec): *MVSEC The Multi Vehicle Stereo Event Camera Dataset*.
- [Zhou et al., ECCV 2018](#Zhou18eccv): *Semi-Dense 3D Reconstruction with a Stereo Event Camera*.
- [Zhou et al., TRO 2021](#Zhou21tro), *Event-based Stereo Visual Odometry*.
- <a name="MGehrig21ral"></a>Gehrig, M., Aarents, W., Gehrig, D., Scaramuzza, D.,  
*[DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://doi.org/10.1109/LRA.2021.3068942)*,  
IEEE Robotics and Automation Letters (RA-L), 2021. [Dataset](http://rpg.ifi.uzh.ch/dsec.html), [PDF](http://rpg.ifi.uzh.ch/docs/RAL21_DSEC.pdf), [Code](https://github.com/uzh-rpg/DSEC), [Video](https://youtu.be/W4yW78y4F7A).
- [Wang et al., IROS 2021 (SHEF)](#Wang21iros), *Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception*, [Project Page](https://github.com/ziweiWWANG/SHEF).
- [Gao et al., RAL 2022](#Gao22ral), *VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM*.
- [Chaney et al., CVPRW 2023](#Chaney23cvprw), *M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset*

## Monocular Object Reconstruction
- [Chen et al., ICVR 2023](#Chen23icvr), *Dense Voxel 3D Reconstruction Using a Monocular Event Camera*.

## Optical Flow
- [DVS/DAVIS Optical Flow Dataset](https://docs.google.com/document/d/1r9sRYANGuDTUcfSSq-sL4sd79SfjHGNRul_10uztDaI/pub) associated to the paper [Rueckauer and Delbruck, FNINS 2016](#Rueckauer16fnins).
- [Bardow et al., CVPR2016](#Bardow16cvpr), [Four sequences](http://wp.doc.ic.ac.uk/pb2114/datasets/)
- [Zhu et al., RAL2018](#Zhu18mvsec): *MVSEC The Multi Vehicle Stereo Event Camera Dataset*.
- [Almatrafi et al. PAMI 2020](#Almatrafi20arxiv): *Distance Surface for Event-Based Optical Flow*. [DVSMOTION20 Dataset](https://sites.google.com/a/udayton.edu/issl/software/dataset?authuser=0)
- [EDFLOW21 Event Driven Optical Flow Camera dataset](https://sites.google.com/view/edflow21/home) associated with the paper [EDFLOW: Event Driven Optical Flow Camera with Keypoint Detection and Adaptive Block Matching](#LiuDelbruck2022tcsvt).
- [Gehrig et al., RAL 2021](#MGehrig21ral): *DSEC: A Stereo Event Camera Dataset for Driving Scenarios*.  
- [EV-IMO Event based Independently Moving Objects dataset](https://better-flow.github.io/evimo/) associated to the paper [EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras](#Mitrokhin19iros) ([motion vector flow](https://github.com/better-flow/evimo/blob/master/tools/evimo_flow.py) added Jan 2022)
- [Chaney et al., CVPRW 2023](#Chaney23cvprw), *M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset*

## Eye Tracking
- [Chen et al., CVPRW 2024](#Chen23biocas), *3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network*
- [Bonazzi et al., CVPRW 2024](#Bonazzi24cvprw), *Retina: Low-Power Eye Tracking with Event Camera and Spiking Hardware*

## Gaze Estimation
- [Angelopoulos et al., IEEE Trans. Vis. Comput. Graphics 2021](#Angelopoulos20arxiv), *Event Based, Near-Eye Gaze Tracking Beyond 10,000Hz*

## Intensity-Image Reconstruction from Events
- [Bardow et al., CVPR2016](#Bardow16cvpr), [Four sequences](http://wp.doc.ic.ac.uk/pb2114/datasets/)
- [Scheerlinck et al., ACCV2018](#Scheerlinck18accv), *Continuous-time Intensity Estimation Using Event Cameras*. [Website](https://cedric-scheerlinck.github.io/continuous-time-intensity-estimation)
- <a name="Scheerlinck19cvprw"></a>Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., Scaramuzza, D.,  
[CED: Color Event Camera Dataset](http://rpg.ifi.uzh.ch/CED.html),  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [Slides](http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_CED_pitch.pdf), [Video pitch](https://youtu.be/BfMjtUQwWnQ).
- [Rebecq et al., TPAMI 2020](#Rebecq20tpami),  
*High Speed and High Dynamic Range Video with an Event Camera*. [Project page](http://rpg.ifi.uzh.ch/E2VID/)
- [High Quality Frames (HQF) dataset](https://drive.google.com/drive/folders/18Xdr6pxJX0ZXTrXW9tK0hC3ZpmKDIt6_) associated to the paper [Stoffregen et al., arXiv 2020](#Stoffregen20arxiv).
- [Wang et al., CVPR 2020](#Wang20cvpr-gef),  
*Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging*. [Project page](https://sites.google.com/view/guided-event-filtering)
- [HDR Hybrid Event-Frame Dataset, TPAMI 2023](#Wang23tpami),  
*An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras*. [Project page](https://github.com/ziweiWWANG/Event-Asynchronous-Filter)


## Visual Odometry and SLAM
- [Combined Dynamic Vision / RGB-D Dataset](http://ebvds.neurocomputing.systems/EBSLAM3D/index.html) associated to the paper [Weikersdorfer et al., ICRA 2014](#Weikersdorfer14icra).
- Barranco, F., Fermüller, C., Aloimonos, Y.,  
*[A Dataset for Visual Navigation with Neuromorphic Methods](https://dx.doi.org/10.3389%2Ffnins.2016.00049),*  
Front. Neurosci. (2016), 10:49.
- <a name="Mueggler17ijrr"></a>Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., Scaramuzza, D.,  
*[The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](http://rpg.ifi.uzh.ch/davis_data.html),*  
Int. J. Robotics Research, 36:2, pp. 142-149, 2017. [PDF](https://arxiv.org/pdf/1610.08336.pdf), [PDF IJRR](http://dx.doi.org/10.1177/0278364917691115), [YouTube](https://youtu.be/bVVBTQ7l36I), [Dataset](http://rpg.ifi.uzh.ch/davis_data.html).
- <a name="Binas17icmlw"></a>Binas, J., Neil, D., Liu, S.-C., Delbruck, T.,  
*[DDD17: End-To-End DAVIS Driving Dataset](https://www.openreview.net/pdf?id=HkehpKVG-),*  
Int. Conf. Machine Learning, Workshop on Machine Learning for Autonomous Vehicles, 2017. [Dataset](http://sensors.ini.uzh.ch/databases.html)
- <a name="Zhu18mvsec"></a>Zhu, A., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., Daniilidis, K.,  
*[The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://doi.org/10.1109/LRA.2018.2800793),*  
IEEE Robotics and Automation Letters (RA-L), 3(3):2032-2039, Feb. 2018. [PDF](https://arxiv.org/abs/1801.10202), [Dataset](https://daniilidis-group.github.io/mvsec/), [YouTube](https://youtu.be/9FaUvvzaHW8).
- [Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](http://rpg.ifi.uzh.ch/direct_event_camera_tracking/index.html) associated to the paper [Gallego et al., PAMI 2018](#Gallego18tpami)
- <a name="Leung18spie"></a>Leung, S., Shamwell, J., Maxey, C., Nothwang, W. D.,  
[Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications](https://doi.org/10.1117/12.2305504),  
Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391T. [PDF](https://www.researchgate.net/publication/325939343)
- [Event-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization](http://rpg.ifi.uzh.ch/direct_event_camera_tracking/index.html) associated to the paper [Bryner et al., ICRA 2019](#Bryner19icra).
- <a name="Delmerico19icra"></a>Delmerico, J., Cieslewski, T., Rebecq, H., Faessler, M., Scaramuzza, D.,  
[Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset](http://rpg.ifi.uzh.ch/uzh-fpv.html),  
IEEE Int. Conf. Robotics and Automation (ICRA), 2019. [PDF](http://rpg.ifi.uzh.ch/docs/ICRA19_Delmerico.pdf), [YouTube](https://youtu.be/G5w4ZcEzvoo), [Project page](http://rpg.ifi.uzh.ch/uzh-fpv.html).
- <a name="Lee19icraw"></a>Lee, A. J., Cho, Y., Yoon, S., Shin, Y., Kim, A.,  
[ViViD: Vision for Visibility Dataset](https://sites.google.com/view/dgbicra2019-vivid/),  
IEEE Int. Conf. Robotics and Automation (ICRA) Workshop: Dataset Generation and Benchmarking of SLAM Algorithms for Robotics and VR/AR, 2019.
- [Mitrokhin et al., IROS 2019](#Mitrokhin19iros).  
*EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras*
- <a name="Hu20itsc"></a>Hu, Y., Binas, J., Neil, D., Liu, S.-C., Delbruck, T.,  
*[DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://arxiv.org/abs/2005.08605)*,  
IEEE Intelligent Transportation Systems Conf. (ITSC), 2020. [Dataset](https://sites.google.com/view/davis-driving-dataset-2020/home), [More datasets](http://sensors.ini.uzh.ch/databases.html)
- <a name="RodriguezGomez21ral"></a>Rodríguez-Gómez, J. P., Tapia, R., Paneque, J. L., Grau, P., Gómez Eguíluz, A., Martínez-de Dios, J. R., Ollero A.,  
*[The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception](https://arxiv.org/pdf/2101.10371)*,  
IEEE Robotics and Automation Letters (RA-L), 2021.
- <a name="Klenk21iros"></a>Klenk S., Chui, J., Demmel, N., Cremers, D.,  
*[TUM-VIE: The TUM Stereo Visual-Inertial Event Data Set](https://vision.in.tum.de/data/datasets/visual-inertial-event-dataset)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021.
- <a name="lee2022vivid++"></a>Lee, A. J., Cho, Y., Shin, Y., Kim, A., Myung, H.,  
[ViViD++: Vision for Visibility Dataset](https://visibilitydataset.github.io/),  
IEEE Robotics and Automation Letters (RA-L), 2022. [Dataset](https://visibilitydataset.github.io/)
- <a name="Gao22ral"></a>Gao, L., Liang, Y., Yang, J., Wu, S., Wang, C., Chen, J., Kneip, L.,  
*[VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM](https://doi.org/10.1109/LRA.2022.3186770)*,  
IEEE Robotics and Automation Letters (RA-L), 7(3):8217-8224, 2022. [PDF](https://arxiv.org/abs/2207.01404), [Dataset](https://star-datasets.github.io/vector/), [MPL Calibration Toolbox](https://github.com/mgaoling/mpl_calibration_toolbox), [MPL Dataset Toolbox](https://github.com/mgaoling/mpl_dataset_toolbox).
- <a name="Chaney23cvprw"></a>Chaney, K., Cladera, F., Wang, Z., Bisulco, A., Hsieh, M.A., Korpela, C., Kumar, V., Taylor, C.J. and Daniilidis, K.,  
*[M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset](https://m3ed.io/)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2023.
[PDF](https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Chaney_M3ED_Multi-Robot_Multi-Sensor_Multi-Environment_Event_Dataset_CVPRW_2023_paper.pdf),
[Dataset](https://m3ed.io/sequences/),
[Code](https://github.com/daniilidis-group/m3ed),
- <a name="Mollica24tits"></a>Mollica, G., Felicioni, S., Legittimo, M., Meli, L., Costante, G. and Valigi, P.,  
*[MA-VIED: A Multisensor Automotive Visual Inertial Event Dataset](http://doi.org/10.1109/TITS.2023.3312355)*,  
IEEE Trans. Intell. Transp. Syst. (T-ITS), vol. 25, no. 1, pp. 214-224, Jan. 2024.
[Dataset](http://sira.diei.unipg.it/supplementary/public/Datasets/MA-VIED),
[GitHub Page](https://github.com/isarlab-department-engineering/MA-VIED),
[Project Page](https://isar.unipg.it/datasets/)
- <a name="Guo24ecrot"></a>Guo, S., and Gallego, G.,  
*[Event Camera Rotation Dataset (ECRot)](https://github.com/tub-rip/ECRot)* introduced with the paper *[CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://github.com/tub-rip/cmax_slam)*,  
IEEE Trans. Robot. (TRO), 2024.

## Segmentation
- [Mitrokhin et al., IROS 2018](#Mitrokhin18iros), Extreme Event Dataset (EED). [Project page and Dataset](http://prg.cs.umd.edu/BetterFlow.html)
- <a name="Mitrokhin19iros"></a>Mitrokhin, A., Ye, C., Fermüller, C., Aloimonos, Y., Delbrück, T.,  
*[EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras](https://doi.org/10.1109/IROS40897.2019.8968520)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2019. [PDF](https://arxiv.org/pdf/1903.07520.pdf), [Dataset](https://better-flow.github.io/evimo), [Project page](http://prg.cs.umd.edu/EV-IMO.html)

## Recognition
- <a name="Orchard15fnins"></a>Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.,  
*[Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://doi.org/10.3389/fnins.2015.00437),*  
Front. Neurosci. (2015), 9:437. [YouTube](https://youtu.be/2RBKNhxHvdw)
    - [Neuromorphic-MNIST (N-MNIST) dataset](http://www.garrickorchard.com/datasets/n-mnist) is a spiking version of the original frame-based MNIST dataset (of handwritten digits). [YouTube](https://youtu.be/6qK97qM5aB4)
    - [The Neuromorphic-Caltech101 (N-Caltech101) dataset](http://www.garrickorchard.com/datasets/n-caltech101) is a spiking version of the original frame-based Caltech101 dataset. [YouTube](https://youtu.be/dxit9Ce5f_E)
- Serrano-Gotarredona,T. and Linares-Barranco, B.,  
*[Poker-DVS and MNIST-DVS. Their History, How They were Made, and Other Details](http://dx.doi.org/10.3389/fnins.2015.00481)*,  
Front. Neurosci. (2015), 9:481.
    - [MNIST-DVS and FLASH-MNIST-DVS datasets](http://www2.imse-cnm.csic.es/caviar/MNISTDVS.html) are based on the original frame-based MNIST dataset. MNIST-DVS are DVS128 recordings of moving MNIST digits (at 3 scales), while FLASH-MNIST-DVS datasets are recorded by flashing the digits on a monitor.
    - [POKER-DVS](http://www2.imse-cnm.csic.es/caviar/POKERDVS.html). From a set of DVS recordings of very fast poker card browsing, 32x32 pixel windows tracking the symbols are cropped. On average each symbol lasts about 10-30ms.
    - [SLOW-POKER-DVS](http://www2.imse-cnm.csic.es/caviar/SLOWPOKERDVS.html). Paper printed poker card symbols are moved at "human speed" in front of a DVS camera and recorded at 128x128 resolution.
- [VISUALISE Predator/Prey Dataset](https://www.dropbox.com/sh/x6nm6zl9rrd7yzn/AAB_Fa5F-Y4fSo1nrIJxc8Xoa?dl=0) associated to the paper [Moeys et al., EBCCSP 2016](#Moeys16ebccsp)
- <a name="Hu16fnins"></a>Hu, Y., Liu, H., Pfeiffer, M., Delbruck, T.,  
*[DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition](https://doi.org/10.3389/fnins.2016.00405),*  
Front. Neurosci. (2016), 10:405. [Dataset](http://dgyblog.com/projects-term/dvs-dataset.html)
- <a name="Liu16fnins"></a>Liu, Q., Pineda-García, G., Stromatias, E., Serrano-Gotarredona, T., Furber, SB.,  
*[Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation](https://doi.org/10.3389/fnins.2016.00496)*,  
Front. Neurosci. (2016), 10:496. [Dataset](https://github.com/qian-liu/benchmarking), [Dataset](https://github.com/NEvision/NE15)
- <a name="dvsgesture_dataset"></a>[DVS128 Gesture Dataset](http://research.ibm.com/dvsgesture/): The dataset that was used to build the real-time gesture recognition system described in [Amir et al., CVPR 2017](#Amir17cvpr).
- <a name="ncars_dataset"></a>[N-CARS Dataset](http://www.prophesee.ai/dataset-n-cars/): A large real-world event-based dataset for car classification.     [Sironi et al., CVPR 2018](#Sironi18cvpr).
- [Mitrokhin et al., IROS 2018](#Mitrokhin18iros) *Event-based Moving Object Detection and Tracking*. [Project page and Dataset](http://prg.cs.umd.edu/BetterFlow.html)
- <a name="atisplanesdataset"></a>[ATIS Plane Dataset](https://www.westernsydney.edu.au/bens/home/reproducible_research/atis_planes), assocated to the paper [Afshar et al., Front. Neurosci. 2018](#Afshar18fnins).    
- <a name="Cheng19cvprw"></a>Cheng, W., Luo, H., Yang, W., Yu, L., Chen, S., Li, W.,  
*[DET: A High-resolution DVS Dataset for Lane Extraction](http://openaccess.thecvf.com/content_CVPRW_2019/papers/EventVision/Cheng_DET_A_High-Resolution_DVS_Dataset_for_Lane_Extraction_CVPRW_2019_paper.pdf),*  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. [Project page](https://spritea.github.io/DET/).
- <a name="Miao19fnbot"></a>Miao, S., Chen, G., Ning, X., Zi, Y., Ren, K., Bing, Z., Knoll, A.,  
*[Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection](https://doi.org/10.3389/fnbot.2019.00038),*  
Front. Neurorobot. (2019). [Dataset](https://github.com/MSZTY/PAFBenchmark)
- <a name="deTournemire20arxiv"></a>de Tournemire, P., Nitti, D., Perot, E., Migliore, D., Sironi, A.,  
*[A Large Scale Event-based Detection Dataset for Automotive](https://arxiv.org/abs/2001.08499)*,  
arXiv, 2020. [Code](https://github.com/prophesee-ai/prophesee-automotive-dataset-toolbox), [News](https://www.prophesee.ai/2020/01/24/prophesee-gen1-automotive-detection-dataset/)
- [N-SOD Dataset](https://drive.google.com/drive/folders/10_bnZ_TOcq7xCtCiDcaDiIO3_Txgr19S) associated to the paper [Ramesh et al., FNINS 2020](#Ramesh20fnins).
- [SL-ANIMALS-DVS Database](http://www2.imse-cnm.csic.es/neuromorphs/index.php/SL-ANIMALS-DVS-Database) associated to the paper [Vasudevan et al., FG 2020](#Vasudevan20fg). Recordings made using the sensitive DVS developed at [IMSE](http://imse-cnm.csic.es).
- <a name="1mpx_detection_dataset"></a> Perot, E., de Tournemire, P., Nitti, D., Masci, J., Sironi, A.,  [1Mpx Detection Dataset](https://www.prophesee.ai/2020/11/24/automotive-megapixel-event-based-dataset/): [Learning to Detect Objects with a 1 Megapixel Event Camera. NeurIPS 2020](#Perot20nips).
- <a name="Cannici21cvprw"></a>Cannici, M., Plizzari, C., Planamente, M., Ciccone, M., Bottino, A., Caputo, B., Matteucci, M.,  
*[N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Cannici_N-ROD_A_Neuromorphic_Dataset_for_Synthetic-to-Real_Domain_Adaptation_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](https://n-rod-dataset.github.io/home/), [YouTube](https://youtu.be/IOZl8MxrfpQ), [Poster](https://tub-rip.github.io/eventvision2021/slides/CVPRW21_NROD_poster.pdf).
- [N-EPIC-Kitchens Dataset of Plizzari et al., CVPR 2022](#Plizzari22cvpr). [Project page](https://github.com/EgocentricVision/N-EPIC-Kitchens)
- [THU<sup>E-ACT</sup>-50 Dataset](https://github.com/lujiaxuan0520/THU-EACT-50) associated to the paper [Gao et al., TPAMI 2023](#Gao23tpami).
- [MS-EVS (N-YoutubeFaces, N-MobiFace, N-SpectralFace or Real-SpectralFace)](https://ms-evs.github.io/) associated to the paper [Himmi et al., WACV 2024](#Himmi24wacv)
- [THU<sup>MV-EACT</sup>-50 Dataset](https://github.com/lujiaxuan0520/THU-MV-EACT-50) associated to the paper [Gao et al., TPAMI 2024](#Gao24tpami).
- <a name="Duarte24dib"></a>Duarte, L., Neto, P.,  
*[Event-based dataset for the detection and classification of manufacturing assembly tasks](https://doi.org/10.1016/j.dib.2024.110340),*  
Data in Brief, 2024. [Dataset](https://doi.org/10.5281/zenodo.10562563), [Code](https://github.com/Robotics-and-AI/DAVIS-data-capture-system).

## Event Denoising
- [DVSNOISE20](https://sites.google.com/a/udayton.edu/issl/software/dataset) associated to the paper [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](#Baldwin20cvpr).
- [DND21 DeNoising Dynamic vision sensors dataset](https://sites.google.com/view/dnd21/datasets?authuser=0) associated to the paper [Low Cost and Latency Event Camera Background Activity Denoising](#GuoDelbruck22pami)
- [Event Flicker Removal Dataset](https://drive.google.com/drive/u/1/folders/1S2LiKrteh4N23SOghA0xU7rC1Q8trNCR) associated to the paper [Wang et al., ICRA 2022](#Wang22icra). [PDF](https://arxiv.org/pdf/2205.08090.pdf), [Project Page](https://github.com/ziweiWWANG/EFR).

## Space Situational Awareness
- [The Event-Based Space Situational Awareness (EBSSA) Dataset](https://www.westernsydney.edu.au/icns/reproducible_research/publication_support_materials/space_imaging) associated to the paper [Event-based Object Detection and Tracking for Space Situational Awareness](#Afshar19arxiv).
- [The Event Based Space Imaging Slew Speed Star Dataset](https://github.com/NicRalph213/ICNS_NORALPH_Event_Based-Space_Imaging-Speed_Dataset) associated to the paper [Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging](#Ralph22arxiv).

## Outdoor Monitoring / Surveillance
- <a name="Bolten21cvprw"></a>Bolten, T., Pohle-Frohlich, R., Tonnies, K. D.,  
*[DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios](https://openaccess.thecvf.com/content/CVPR2021W/EventVision/papers/Bolten_DVS-OUTLAB_A_Neuromorphic_Event-Based_Long_Time_Monitoring_Dataset_for_Real-World_CVPRW_2021_paper.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](http://dnt.kr.hsnr.de/DVS-OUTLAB/), [YouTube](https://youtu.be/4wV0ejrcDo0)
- <a name="Verma24cvpr"></a>Verma, A.A., Chakravarthi, B., Vaghela, A., Wei, H., Yang, Y.,  
*[eTraM: Event-based Traffic Monitoring Dataset](https://arxiv.org/abs/2403.19976)*,  
IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2024. [Project page and Dataset](https://eventbasedvision.github.io/eTraM/).


<br><br>
<a name="software"></a>
# Software

<a name="drivers"></a>
## Drivers
- [jAER (java Address-Event Representation) project](http://jaerproject.org/). *Real time sensory-motor processing for event-based sensors and systems*. [github page](https://github.com/SensorsINI/jaer). [jAER User Guide](https://docs.google.com/document/d/1fb7VA8tdoxuYqZfrPfT46_wiT1isQZwTHgX8O22dJ0Q/edit?usp=sharing)
- [caer (AER event-based framework, written in C, targeting embedded systems)](https://github.com/inilabs/caer)
- [libcaer (Minimal C library to access, configure and get/send AER data from sensors or to/from neuromorphic processors)](https://gitlab.com/inivation/dv/libcaer)
- [evl (Open Source Computer Vision Library for Event-based camera and vision for C++)](https://github.com/EventVisionLibrary/evl)
- [ROS (Robotic Operating System)](https://github.com/uzh-rpg/rpg_dvs_ros)
- [YARP (Yet Another Robot Platform)](https://github.com/robotology/event-driven)
- [Prophesee ROS Wrapper](https://github.com/prophesee-ai/prophesee_ros_wrapper) ROS driver and messages for Prophesee event-based sensors
- [Prophesee camera plugins](https://docs.prophesee.ai/stable/installation/camera_plugins.html)
- [CeleX5 ROS Wrapper](https://github.com/kehanXue/CeleX5-ROS) A ROS driver and some other tools for [CeleX5_MP](http://www.celepixel.com/#/Samples) event-based sensor (which has a high resolution at 1280×800)
- [PyAER](https://github.com/duguyue100/pyaer) A lightweight python wrapper of libcaer; used for e.g. [Dextra](https://github.com/SensorsINI/dextra-roshambo-python) and [Trixsy](https://github.com/SensorsINI/joker-network) robots and [PDAVIS e2p demo](https://github.com/SensorsINI/e2p).

<a name="synchronization"></a>
## Synchronization
- [Sync Toolbox](https://github.com/sjtuyuxuan/sync_toolbox). This open-source toolbox provides a QT-based GUI to allow easy access for hardware-level multi-sensor synchronization (Prophesee Gen 3.1 included and tested). After proper configuration of the software, users can seamlessly record new ROS bags.
- <a name="Li23icira"></a>Li, W.X., Dong, Y., Qiu, S.Q., Han, B.,  
*[Hardware-Free Event Cameras Temporal Synchronization Based on Event Density Alignment](https://link.springer.com/chapter/10.1007/978-981-99-6498-7_6)*,  
Int. Conf. Intell. Robotics and Applications (ICIRA), 2023. 

<a name="calibration"></a>
## Lens Calibration
- [Lens focus adjustment](https://github.com/uzh-rpg/rpg_dvs_ros/tree/master/dvs_calibration#focus-adjustment) or [this other source](https://github.com/ethz-asl/kalibr/wiki/calibrating-the-vi-sensor#2-setting-the-focus).
- For the DAVIS: use the grayscale frames to calibrate the optics of both frames and events.
    - ROS camera calibrator ([monocular](http://wiki.ros.org/camera_calibration/Tutorials/MonocularCalibration) or [stereo](http://wiki.ros.org/camera_calibration/Tutorials/StereoCalibration))
     - [Kalibr software](https://github.com/ethz-asl/kalibr/wiki/multiple-camera-calibration) by ASL - ETH.
     - [Basalt software](https://gitlab.com/VladyslavUsenko/basalt/-/blob/master/doc/Calibration.md) by TUM.
- For the DAVIS camera and IMU calibration: [kalibr software](https://github.com/ethz-asl/kalibr/wiki/camera-imu-calibration) by ASL - ETH, using the grayscale frames.
- For the DVS (events-only):
    - [Calibration using blinking LEDs or computer screens](https://github.com/uzh-rpg/rpg_dvs_ros/tree/master/dvs_calibration) by RPG - UZH.
    - [DVS camera calibration](https://github.com/gorchard/DVScalibration) by G. Orchard.
    - [DVS camera calibration](https://github.com/VLOGroup/dvs-calibration) by VLOGroup at TU Graz.
- For Prophesee Camera (events-only):
    - [Focus adjustment and calibration using blinking LEDs or computer screens](https://docs.prophesee.ai/stable/samples/modules/calibration/index.html)
- <a name="Song18wrcsara"></a>Song, R., Jiang, Z., Li, Y., Shan, Y., Huang, K.,  
*[Calibration of Event-based Camera and 3D LiDAR](https://doi.org/10.1109/WRC-SARA.2018.8584215)*,  
WRC Symposium on Advanced Robotics and Automation (WRC SARA), 2018.
- <a name="DominguezMorales19access"></a>Dominguez-Morales, M. J., Jimenez-Fernandez, A., Jimenez-Moreno, G., Conde, C., Cabello, E., Linares-Barranco, A.,  
*[Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors](https://doi.org/10.1109/ACCESS.2019.2943160)*,  
IEEE Access, 7:138415-138425, 2019.
- <a name="Wang19acra"></a>Wang, Z., Ng, Y., van Goor, P., Mahony., R.,  
*[Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://arxiv.org/pdf/2012.09378)*,  
Australasian Conf. Robotics and Automation (ACRA) 2019. [PDF](https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/includes/files/pap135s1-file1.pdf), [Code and Data](https://github.com/ziweiWWANG/Event-Camera-Calibration).
- <a name="Dubeau20arxiv"></a>Dubeau, E., Garon, M., Debaque, B., de Charette, R., Lalonde, J.-F.,  
*[RGB-DE: Event Camera Calibration for Fast 6-DOF Object Tracking](https://arxiv.org/pdf/2006.05011.pdf)*,  
arXiv, 2020.
- <a name="Wang20jsen"></a>Wang, G., Feng, C., Hu, X., Yang, H.,  
*[Temporal Matrices Mapping Based Calibration Method for Event-Driven Structured Light Systems](https://doi.org/10.1109/JSEN.2020.3016833)*,  
IEEE Sensors Journal, 2020.
- <a name="Muglikar21cvprw"></a>Muglikar, M., Gehrig, M., Gehrig, D., Scaramuzza, D.,  
*[How to Calibrate Your Event Camera](http://rpg.ifi.uzh.ch/docs/CVPRW21_Muglikar.pdf)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [Project page](https://github.com/uzh-rpg/e2calib), [YouTube](https://youtu.be/UlTd9Z25WGM), [Code](https://github.com/uzh-rpg/e2calib).
- <a name="Huang21iros"></a>Huang, K., Wang, Y., Kneip, L.,  
*[Dynamic Event Camera Calibration](https://arxiv.org/abs/2107.06749)*,  
IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021. [Video](https://www.bilibili.com/video/BV1ey4y1j7Ke?share_source=copy_web), [Code](https://github.com/MobilePerceptionLab/EventCameraCalibration).
- <a name="Bao23arxiv"></a>Bao, Y., Sun, L., Ma, Y., Gu, D., Wang, K.,  
*[Improving Fast Auto-Focus with Event Polarity](https://arxiv.org/abs/2303.08611)*,  
Arxiv, 2023.
- <a name="Cocheteux24cvprw"></a>Cocheteux, M., Moreau, J., Davoine, F.,  
*[MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration](https://openaccess.thecvf.com/content/CVPR2024W/WAD/html/Cocheteux_MULi-Ev_Maintaining_Unperturbed_LiDAR-Event_Calibration_CVPRW_2024_paper.html)*,  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2024.


<a name="software-algorithms"></a>
## Algorithms
- [jAER event-processing filters](https://docs.google.com/document/d/1fb7VA8tdoxuYqZfrPfT46_wiT1isQZwTHgX8O22dJ0Q/edit#bookmark=id.a27r41y53r3t) in the [jAER (java Address-Event Representation) project](http://jaerproject.org/)
- [A collection of tracking and detection algorithms](https://github.com/robotology/event-driven) using the YARP framework
- [Some detection and tracking algorithms](https://github.com/EventVisionLibrary/evl) in EVL
- Prophesee Open Source library - [OpenEB](https://github.com/prophesee-ai/openeb)
- **Optical Flow**
    - [LocalPlanesFlow](https://github.com/SensorsINI/jaer/blob/master/src/ch/unizh/ini/jaer/projects/rbodo/opticalflow/LocalPlanesFlow.java), inspired by the paper [Benosman et al., TNNLS 2014](#Benosman14tnnls).
    - [Several algorithms compared](https://github.com/SensorsINI/jaer/tree/master/src/ch/unizh/ini/jaer/projects/rbodo/opticalflow) in the paper by [Rueckauer and Delbruck, FNINS 2016](#Rueckauer16fnins).
    - [Event-Lifetime estimation](https://www.github.com/uzh-rpg/rpg_event_lifetime), associated to the paper [Mueggler et al., ICRA 2015](#Mueggler15icra).
    - [EV-FlowNet](https://github.com/daniilidis-group/EV-FlowNet), associated to the paper [Zhu et al., RSS 2018](#Zhu18rss).
- **Feature Tracking**
    - [Event-based Feature Tracking with Probabilistic Data Association](https://github.com/daniilidis-group/event_feature_tracking), associated to the papers [Zhu et al., ICRA 2017](#Zhu17icra) and [Zhu et al., CVPR 2017](#Zhu17cvpr).
    - [Tracking code](https://github.com/uzh-rpg/rpg_eklt) associated to the paper [Gehrig et al., IJCV 2019]("#Gehrig19ijcv)".
    - [Evaluation code](https://github.com/uzh-rpg/rpg_feature_tracking_analysis) associated to the paper [Gehrig et al., IJCV 2019]("#Gehrig19ijcv)".
- **Intensity-Image reconstruction from events**
    - [Code for intensity reconstruction](https://github.com/uzh-rpg/rpg_image_reconstruction_from_events), inspired by the paper [Kim et al., BMVC 2014](#Kim14bmvc).
    - [DVS Reconstruction code](https://github.com/VLOGroup/dvs-reconstruction) associated to the paper [Reinbacher et al., BMVC 2016](#Reinbacher16bmvc).
    - [High-pass filter code](https://github.com/cedric-scheerlinck/dvs_image_reconstruction) associated to the paper [Scheerlinck et al., ACCV 2018](#Scheerlinck18accv)
    - [E2VID code](https://github.com/uzh-rpg/rpg_e2vid) associated to the paper [Rebecq et al., TPAMI 2020](#Rebecq20tpami).
    - [EVREAL code](https://github.com/ercanburak/EVREAL) associated to the paper [Ercan et al., CVPRW 2023](#Ercan23cvprw).
    - [HyperE2VID code](https://github.com/ercanburak/HyperE2VID) associated to the paper [Ercan et al., TIP 2024](#Ercan24tip).
- **Localization and Ego-Motion Estimation**
    - [Panoramic tracking code](https://github.com/VLOGroup/dvs-panotracking) associated to the paper [Reinbacher et al., ICCP 2017](#Reinbacher17iccp).
- **Pattern Recognition**
    - [A simple spiking neural network for recognition](http://www.garrickorchard.com/code/hfirst) associated to the paper [Orchard et al., TPAMI 2015](#Orchard15tpami).

<a name="software-utilities"></a>
## Utilities
- [Process AEDAT](https://github.com/SensorsINI/processAEDAT): useful scripts to work with data from jAER and cAER.
- [Matlab functions in jAER project](https://sourceforge.net/p/jaer/code/HEAD/tree/scripts/matlab/)
- [AEDAT Tools](https://github.com/simbamford/AedatTools/): scripts for Matlab and Python to work with aedat files.
- [aedat4to2](https://github.com/SensorsINI/aedat4to2): Python/DV script to convert AEDAT4 from DV to AEDAT2 for jAER.
- [aedat4tomat](https://github.com/bald6354/aedat4tomat): Python/DV script to convert AEDAT4 from DV to matlab file.
- [Matlab AER functions](https://github.com/gorchard/Matlab_AER_vision_functions) by G. Orchard. Some basic functions for filtering and displaying AER vision data, as well as making videos.
- [Python code for AER vision data](https://github.com/gorchard/event-Python) by G. Orchard.
- [edvstools](https://github.com/Danvil/edvstools), by D. Weikersdorfer: A collection of tools for the embedded Dynamic Vision Sensor eDVS.
- [Tarsier](https://github.com/neuromorphic-paris/tarsier) Framework for event-based Vision in C++.
- [events_h52bag](https://github.com/tub-rip/events_h52bag) C++ code to convert event data from HDF5 to ROSbags.
- [events_bag2h5](https://github.com/mgaoling/mpl_dataset_toolbox) Python code to convert event data from ROSbags to HDF5.
- [CelexMatlabToolbox](https://github.com/yucicheung/CelexMatlabToolbox) by Yuxin Zhang. Tools to decode events generated by CeleX IV DVS, visualize them and denoise.
- [Loris](https://github.com/neuromorphic-paris/loris) Python package to read files from neuromorphic cameras.
- Marcireau A., Ieng S.-H., Benosman R.,  
*[Sepia, Tarsier, and Chameleon: A Modular C++ Framework for Event-Based Computer Vision](https://doi.org/10.3389/fnins.2019.01338)*,  
Front. Neurosci. (2020), 13:1338. [Code](https://github.com/neuromorphic-paris/tutorials)
- [BIMVEE](https://github.com/event-driven-robotics/bimvee) Python tools for Batch Import, Manipulation, Visualisation and Export of Events and other timestamped data. Imports from various file formats into a common workspace format, including native Python import of rosbags.
- [Tonic](https://github.com/neuromorphs/tonic) provides publicly available event datasets and data transformations much like Torchvision/audio.
- Prophesee automotive dataset toolbox, [Code](https://github.com/prophesee-ai/prophesee-automotive-dataset-toolbox)
- [dv_ros](https://github.com/kehanXue/dv_ros) ROS package for accumulating event frames with iniVation Dynamic Vision System's dv-sdk.
- [dvs_event_server](https://github.com/robin-shaun/dvs_event_server) ROS package used to transport "dvs/events" ROS topic to Python through protobuf and zmq, because Python ROS callback has a large delay.
- [AEStream](https://github.com/norse/aestream/) A fast C++ library with a Python interface for streaming Address Event representations directly from Inivation and Prophesee cameras to various sources, such as STDOUT, UDP (network), or [PyTorch](https://pytorch.org/).<br/>
  - Pedersen, J., Conradt, J.,<br/>
  [AEStream: Accelerated event-based processing with coroutines](https://dl.acm.org/doi/10.1145/3584954.3584997),<br/>
  Proc. Annual Neuro-Inspired Computational Elements Conf. (NICE), 2023, pp. 86-91.
- [AEDAT decoder](https://github.com/neuromorphicsystems/aedat) A fast AEDAT 4 Python reader, with a Rust underlying implementation.
- [aedat-rs](https://github.com/ac-freeman/aedat-rs) Standalone Rust library for decoding AEDAT 4 files for use in bespoke Rust event systems.
- [expelliarmus](https://github.com/fabhertz95/expelliarmus) A pip-installable Python library to decode DAT, EVT2 and EVT3 files generated by Prophesee cameras to structured NumPy arrays.
- [ADΔER](https://github.com/ac-freeman/adder-codec-rs/) A suite of tools for transcoding, inspecting, visualizing, lossy compressing, and building applications for a unified intensity event representation. Supports iniVation, Prophesee, and frame-based video sources.
  - <a name="Freeman23mmsys"></a>Freeman, A.,  
    *[The ADΔER Framework: Tools for Event Video Representations](https://doi.org/10.1145/3587819.3593028)*,  
    ACM Multimedia Systems (MMSys) Doctoral Symposium, 2023. [PDF](https://dl.acm.org/doi/pdf/10.1145/3587819.3593028), [Code](https://github.com/ac-freeman/adder-codec-rs/).
  - <a name="Freeman24arxiv"></a>Freeman, A.,  
    *[An Open Software Suite for Event-Based Video](https://arxiv.org/abs/2401.17151)*,  
    arXiv, 2024.


<a name="processors-platforms"></a>
# Neuromorphic Processors and Platforms
- <a name="Hofstaetter11icecs"></a>Hoffstaetter, M., Belbachir, N., Bodenstorfer, E., Schoen, P.,  
*[Multiple Input Digital Arbiter with Timestamp Assignment for Asynchronous Sensor Arrays](https://doi.org/10.1109/ICECS.2006.379767)*,  
IEEE Int. Conf. Electronics, Circuits and Systems (ICECS), 2006.
- <a name="Belbachir08icecs"></a>Belbachir, A., Hofstaetter, M., Reisinger, K., Litzenberger, M., Schoen, P.,  
*[High-Precision Timestamping and Ultra High-Speed Arbitration of Transient Pixels' Events](https://doi.org/10.1109/ICECS.2008.4674996)*,  
Int. Conf. on Electronics, Circuits and Systems (ICECS), 2008.
- <a name="Hofstaetter09biocas"></a>Hoffstaetter, M., Schoen, P., Posch, C.,  Bauer, D.,  
*[An integrated 20-bit 33/5M events/s AER sensor interface with 10ns time-stamping and hardware-accelerated event pre-processing](https://doi.org/10.1109/BIOCAS.2009.5372034)*,  
IEEE Biomedical Circuits and Systems Conference (BioCAS), 2009.
- <a name="Hofstaetter11icecs"></a>Hoffstaetter, M., Litzenberger, M., Matolin, D., Posch, C.,  
*[Hardware-accelerated address-event processing for high-speed visual object recognition](https://doi.org/10.1109/ICECS.2011.6122221)*,  
IEEE Int. Conf. Electronics, Circuits, and Systems (ICECS), 2011.
- [Dynamic Neuromorphic Asynchronous Processor (DYNAP) by aiCTX AG](https://ai-ctx.com/products/dynap/)
  - <a name="Qiao15fnins"></a>Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., Indiveri, G.,  
  *[A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses](https://doi.org/10.3389/fnins.2015.00141),*  
  Front. Neurosci. (2015), 9:141. [PDF](https://capocaccia.ethz.ch/capo/raw-attachment/wiki/2015/hybrid15/frontiers14-nlp.pdf)
  - <a name="Indiveri15iedm"></a>Indiveri, G., Qiao, N., Corradi, F.,  
  *[Neuromorphic Architectures for Spiking Deep Neural Networks](https://doi.org/10.1109/IEDM.2015.7409623)*,  
  IEEE Int. Electron Devices Meeting (IEDM), 2015. [PDF](http://ncs.ethz.ch/pubs/pdf/Indiveri_etal15.pdf)
- <a name="Wiesmann12cvprw"></a>Wiesmann, G., Schraml, S., Litzenberger, M., Belbachir, A. N., Hofstatter, M., Bartolozzi, C.,  
*[Event-driven embodied system for feature extraction and object recognition in robotic applications](https://doi.org/10.1109/CVPRW.2012.6238898),*  
IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
- <a name="Galluppi14icra"></a>Galluppi, F., Denk, C., Meiner, M. C., Stewart, T. C., Plana, L. A., Eliasmith, C., Furber, S., Conradt, J.,  
*[Event-based neural computing on an autonomous mobile platform](https://doi.org/10.1109/ICRA.2014.6907270),*  
IEEE Int. Conf. Robotics and Automation (ICRA), 2014. [PDF](http://compneuro.uwaterloo.ca/files/publications/galluppi.2014.pdf)
- <a name="Graf14CSEDU"></a>Graf, R., King, R., Belbachir, A.,  
*[Braille Vision Using Braille Display and Bio-inspired Camera](http://dx.doi.org/10.5220/0004949302140219)*,  
Int. Conf. Computer Supported Education (CSEDU), SCITEPRESS Digital Library, (2014), pp. 214 - 219.
- <a name="Sengupta21iscas"></a>Sengupta, J. P., Villemur, M., Mendat, D. R., Tognetti, G., Andreou, A. G.,  
*[Architecture and Algorithm Co-Design Framework for Embedded Processors in Event-Based Cameras](https://doi.org/10.1109/ISCAS51556.2021.9401246)*,  
IEEE Int. Symp. Circuits and Systems (ISCAS), 2020, pp. 1-5.
- <a name="Gao22fpga"></a>Gao, Y., Wang, S., So, H. K. H.,  
*[REMOT: A Hardware-Software Architecture for Attention-Guided Multi-Object Tracking with Dynamic Vision Sensors on FPGAs](https://doi.org/10.1145/3490422.3502365)*,  
ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays (FPGA), 2022. [Code](https://github.com/CASR-HKU/REMOT-FPGA-22)


<a name="teaching"></a>
# Courses (Educational content)
- [Event-based Robot Vision at TU Berlin](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision).
    - [YouTube videos and Slides with links](https://www.youtube.com/channel/UCuuIEatPpBZQkQ_kSoeZtCQ/videos)
- [Projects course: Bio-inspired Computer (Event-based) Vision at TU Berlin](https://sites.google.com/view/guillermogallego/teaching/bio-inspired-computer-vision)

<br><br>
<a name="theses"></a>
# Theses and Dissertations

<a name="theses-phd"></a>
## Dissertations
- <a name="Mahowald92PhD"></a>Mahowald, M.,  
*[VLSI Analogs of Neuronal Visual Processing: A Synthesis of Form and Function](http://resolver.caltech.edu/CaltechTHESIS:09122011-094355148)*,  
Ph.D. thesis, California Inst. Of Technology, Pasadena, CA, 1992. [PDF](http://www.ini.uzh.ch/~tobi/papers/mishathesis.pdf)  
She won the Caltech's Clauser prize for the best PhD thesis for this work, which included the silicon retina, AER communication, and a beautiful stereopsis chip.
    - Kluwer book from Misha’s thesis: Mahowald, M., *[An Analog VLSI System for Stereoscopic Vision](https://play.google.com/store/books/details?id=nYbc2XTlweMC)*. Boston: Springer Science & Business Media, 1994.
- <a name="Delbruck93PhD"></a>Delbrück, T.,  
*[Investigations of Analog VLSI Visual Transduction and Motion Processing](http://resolver.caltech.edu/CaltechETD:etd-07022004-144710)*,  
Ph.D. Thesis. California Inst. Of Technology, Pasadena, CA, 1993. [PDF](https://www.ini.uzh.ch/~tobi/anaprose/thesis/tobithesis.pdf)
- <a name="Lichtsteiner06PhD"></a>Lichtsteiner, P.,  
*[A temporal contrast vision sensor](https://doi.org/10.3929/ethz-a-005279479)*,  
Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2006. [PDF](http://www.ini.uzh.ch/~tobi/papers/lichtsteinerThesis2006.pdf)
- <a name="Matolin10PhD"></a>Matolin, D.,  
*[Asynchronous CMOS image sensor with extended dynamic range and suppression of time-redundant data](http://docplayer.org/docview/27/9947799/#file=/storage/27/9947799/9947799.pdf)*,  
Ph.D. Thesis, TU Dresden & AIT, deutsch, 2010.
- <a name="Berner11PhD"></a>Berner, R.,  
*[Building Blocks for Event-Based Sensors](https://doi.org/10.3929/ethz-a-006838001)*,  
Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2011. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/153098/eth-5044-02.pdf?sequence=2&isAllowed=y)
- <a name="Ni13PhD"></a>Ni, Z.,  
*[Asynchronous Event Based Vision:  Algorithms and Applications to Microrobotics](https://tel.archives-ouvertes.fr/tel-00916995/document/)*,  
Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2013.
- <a name="Carneiro14PhD"></a>Carneiro, J.,  
*[Asynchronous Event-Based 3D Vision](http://www.theses.fr/2014PA066593.pdf)*,  
Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2014.
- <a name="Weikersdorfer14PhD"></a>Weikersdorfer, D.,  
*[Efficiency by Sparsity: Depth-Adaptive Superpixels and Event-based SLAM](http://nbn-resolving.de/urn:nbn:de:bvb:91-diss-20140701-1173294-0-6)*,  
Ph.D. Thesis, Technical University of Munich, Munich, Germany, 2014. [PDF](https://mediatum.ub.tum.de/download/1173294/1173294.pdf)
- <a name="Borer14PhD"></a>Borer, D. J.,  
*[4D Flow Visualization with Dynamic Vision Sensors](http://dx.doi.org/10.3929/ethz-a-010344783)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2014. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/95489/eth-47156-02.pdf?sequence=2&isAllowed=y)
- <a name="Yang15PhD"></a>Yang, M.,  
*[Silicon Retina and Cochlea with Asynchronous Delta Modulator for Spike Encoding](https://doi.org/10.3929/ethz-a-010636883)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2015.
- <a name="Brandli15PhD"></a>Brändli, C.,  
*[Event-Based Machine Vision](http://dx.doi.org/10.3929/ethz-a-010402138)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2015. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/99543/eth-47528-02.pdf?sequence=2&isAllowed=y)
- <a name="Lagorce15PhD"></a>Lagorce, X.,  
*[Computational methods for event-based signals and applications](https://tel.archives-ouvertes.fr/tel-01592392)*,  
Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2015. [PDF](https://tel.archives-ouvertes.fr/tel-01592392v2/document)
- <a name="Kogler16PhD"></a>Kogler, J.,  
*[Design and evaluation of stereo matching techniques for silicon retina cameras](https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-2178)*,  
Ph.D. Thesis, Technische Universität Wien, Vienna, Austria, 2016. [PDF](http://repositum.tuwien.ac.at/obvutwhs/download/pdf/1310930?originalFilename=true)
- <a name="Moeys16PhD"></a>Moeys, D. P.,  
*[Analog and digital implementations of retinal processing for robot navigation systems](https://doi.org/10.3929/ethz-a-010897825)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2016. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/156363/eth-50898-02.pdf?sequence=2&isAllowed=y)
- <a name="Cohen16PhD"></a>Cohen, G. K.,  
*[Event-Based Feature Detection, Recognition and Classification](https://tel.archives-ouvertes.fr/tel-01426001)*,  
Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2016. [PDF](https://tel.archives-ouvertes.fr/tel-01426001/document)
- <a name="Li17PhD"></a>Li, C.,  
*[Two-stream vision sensors](https://doi.org/10.3929/ethz-b-000164862)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2017.
- <a name="Neil17PhD"></a>Neil, D.,  
*[Deep Neural Networks and Hardware Systems for Event-driven Data](https://doi.org/10.3929/ethz-b-000168865)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2017. [PDF](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/168865/phd_thesis.pdf?sequence=3&isAllowed=y)
- <a name="Mueggler17PhD"></a>Mueggler, E.,  
*[Event-based Vision for High-Speed Robotics](http://rpg.ifi.uzh.ch/docs/PhD17_Mueggler.pdf)*,  
Ph.D. Thesis, University of Zurich, Zurich, Switzerland, 2017.
- <a name="Kim17PhD"></a>Kim, H.,  
*[Real-time visual SLAM with an event camera](http://hdl.handle.net/10044/1/59704)*,  
Ph.D. Thesis, Imperial College London, United Kingdom, 2017.
- <a name="Huang18PhD"></a>Huang, J.,  
*[Asynchronous high-speed feature extraction image sensor (CelePixel)](http://hdl.handle.net/10220/46625)*,  
Ph.D. Thesis, Nanyang Technological University, Singapore, 2018.
- <a name="Gibson18PhD"></a>Gibson, T. T.,  
*[Inspired by nature: timescale-free and grid-free event-based computing with spiking neural networks](https://doi.org/10.14264/uql.2018.664)*,  
Ph.D. Thesis, The University of Queensland, Brisbane, Australia, 2018.
- <a name="Everding18PhD"></a>Everding, L.,  
*[Event-Based Depth Reconstruction Using Stereo Dynamic Vision Sensors](https://mediatum.ub.tum.de/doc/1445947/1445947.pdf)*,  
Ph.D. Thesis, Technical University of Munich, Munich, Germany, 2018.
- <a name="Seifozzakerini18PhD"></a>Seifozzakerini, S.,  
*[Analysis of object and its motion in event-based videos](https://dr.ntu.edu.sg//handle/10220/46126)*,  
Ph.D. Thesis, Nanyang Technological University, Singapore, 2018.
- <a name="Martel19PhD"></a>Martel, J.,  
*[Unconventional Processing with Unconventional Visual Sensing. Parallel, Distributed and Event Based Vision Algorithms & Systems](https://doi.org/10.3929/ethz-b-000362900)*,  
Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2019.
- <a name="Bardow19PhD"></a>Bardow, P. A.,  
*[Estimating General Motion and Intensity from Event Cameras](https://www.doc.ic.ac.uk/~ajd/Publications/Bardow-P-2019-PhD-Thesis.pdf)*,  
Ph.D. Thesis, Imperial College London, United Kingdom, 2019.
- <a name="Ye19PhD"></a>Ye, C.,  
*[Learning of Dense Optical Flow, Motion and Depth, from Sparse Event Cameras](https://doi.org/10.13016/fhqf-g7xr)*,  
Ph.D. Thesis, University of Maryland, USA, 2019.
- <a name="Liu19PhD"></a>Liu, H.,  
*[Neuromorphic Vision for Robotic Tracking and Navigation](https://doi.org/10.3929/ethz-b-000385246)*,  
Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2019.
- <a name="Zhu19PhD"></a>Zhu, A. Z.,  
*[Event-Based Algorithms for Geometric Computer Vision](https://repository.upenn.edu/edissertations/3566)*,  
Ph.D. Thesis, University of Pennsylvania, USA, 2019.
- <a name="Rebecq19PhD"></a>Rebecq, H.,  
*[Event Cameras: from SLAM to High Speed Video](http://rpg.ifi.uzh.ch/docs/PhD19_Rebecq.pdf)*,  
Ph.D. Thesis, University of Zurich, Zurich, Switzerland, 2019.
- <a name="Kaiser20PhD"></a>Kaiser, J.,  
*[Synaptic Learning for Neuromorphic Vision](https://publikationen.bibliothek.kit.edu/1000122690)*,  
Ph.D. Thesis, Karlsruher Instituts für Technologie (KIT), Karlsruhe, Germany, 2020.
- <a name="Wang20PhD"></a>Wang, Z. (Winston),  
*[Synergy of physics and learning-based models in computational imaging and display](https://dx.doi.org/10.13140/RG.2.2.19514.62404)*,  
Ph.D. Thesis, Northwestern University, 2020. [YouTube](https://youtu.be/VEJxdNok-K0).
- <a name="Mitrokhin20PhD"></a>Mitrokhin, A.,  
*[Motion Segmentation and Egomotion Estimation with Event-Based Cameras](https://doi.org/10.13016/e5vs-hwzw)*,  
Ph.D. Thesis, University of Maryland, USA, 2020.
- <a name="Scheerlinck21PhD"></a>Scheerlinck, C.,  
*[How to See with an Event Camera](https://doi.org/10.25911/5JJS-N433)*,  
Ph.D. Thesis, Australian National University, Canberra, Australia, 2021. [PDF](https://openresearch-repository.anu.edu.au/bitstream/1885/216784/1/Scheerlinck_Thesis_2021.pdf)
- <a name="Stoffregen21PhD"></a>Stoffregen, T.,  
*[Motion Estimation by Focus Optimisation: Optic Flow and Motion Segmentation with Event Cameras](https://doi.org/10.26180/14531871.v1)*,  
Ph.D. Thesis, Monash University, Melbourne, Australia, 2021.
- <a name="Monforte21PhD"></a>Monforte, M.,  
*[Trajectory Prediction with Event-Based Cameras for Robotics Applications](http://hdl.handle.net/11567/1047290)*,  
Ph.D. Thesis, Italian Institute of Technology, Genoa, Italy, 2021. [PDF](https://iris.unige.it/retrieve/handle/11567/1047290/534319/phdunige_4458940.pdf)
- <a name="Lenz21PhD"></a>Lenz, G.,  
*[Neuromorphic algorithms and hardware for event-based processing](https://tel.archives-ouvertes.fr/tel-03474197)*,  
Ph.D. Thesis, Sorbonne University, Paris, France, 2021. [PDF](https://tel.archives-ouvertes.fr/tel-03474197/document)
- <a name="Alzugaray22Phd"></a>Alzugaray, I.,  
*[Event-driven Feature Detection and Tracking for Visual SLAM](https://doi.org/10.3929/ethz-b-000541700)*,   
Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2022. [PDF](https://doi.org/10.3929/ethz-b-000541700)
- <a name="Liu22Phd"></a>Liu, D.,  
*[Motion Estimation Using an Event Camera](https://hdl.handle.net/2440/136406)*,   
Ph.D. Thesis, University of Adelaide, Adelaide, Australia, 2022.
- <a name="Chamorro22Phd"></a>Chamorro, W. O.,  
*[Event-based Simultaneous Localization and Mapping](https://www.iri.upc.edu/thesis/show/136)*,  
Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2023. [PDF](http://www.iri.upc.edu/people/cetto/William_thesis/Chamorro_phdthesis.pdf)
- <a name="Rodriguez23Phd"></a>Rodríguez-Gómez, J.P.,  
*[Event-based Perception for Aerial Robots: From Multirotors to Ornithopters](https://idus.us.es/handle/11441/147735)*,  
Ph.D. Thesis, University of Seville, Seville, Spain, 2023. [PDF](https://grvc.us.es/newweb/wp-content/uploads/2023/06/Thesis_JPRG.pdf)
- <a name="Shiba23Phd"></a>Shiba, S.,  
*[Estimating motion with an event camera](https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO50002002-20236069-0003)*,  
Ph.D. Thesis, Keio University, Tokyo, Japan, 2023. [PDF](https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/download.php/KO50002002-20236069-0003.pdf?file_id=174804)
- <a name="Elshair24Phd"></a>El Shair, Z.,    
*[Advancing Neuromorphic Event-Based Vision Methods for Robotic Perception Tasks](https://dx.doi.org/10.7302/8116)*,    
Ph.D. Thesis, University of Michigan-Dearborn, USA, 2024. [PDF](https://deepblue.lib.umich.edu/bitstream/handle/2027.42/177658/El_Shair_dissertation_Advanced_Neuromorphic_Event_Based_Vision_Methods.pdf?sequence=1&isAllowed=y)
- <a name="Freeman24Phd"></a>Freeman, A.,  
*[Rethinking Video with a Universal Event-Based Representation](https://doi.org/10.17615/5bsv-bz25)*,    
Ph.D. Thesis, University of North Carolina, Chapel Hill, North Carolina, USA, 2024. [PDF](https://arxiv.org/pdf/2408.06248)
- See also [Theses from Delbruck's group at INI](https://www.ini.uzh.ch/~tobi/wiki/doku.php?id=publications#phd_thesis)



<a name="theses-master"></a>
## Master's (and Bachelor's) Theses
- <a name="Reisinger06MSc"></a>Reisinger, K.,  
*[EMC testing on Silicon Retinas](https://permalink.catalogplus.tuwien.at/AC05792670)*,  
MSc. Thesis, TU Wien & AIT, Austria, 2006.
- <a name="Nowakowska11MSc"></a>Nowakowska, A.,  
*[Recognition of a vision approach for fall detection using a biologically inspired dynamic stereo vision sensor](http://repositum.tuwien.ac.at/obvutwhs/download/pdf/1623418?originalFilename=true)*,  
MSc. Thesis, TU Wien & AIT, Austria, 2011.
- <a name="Reingruber11MSc"></a>Reingruber, H.,  
*[An Asynchronous Data Interface for Event-based Stereo Matching](http://repositum.tuwien.ac.at/obvutwhs/download/pdf/1621666?originalFilename=true)*,  
MSc. Thesis, TU Wien & AIT, Austria, 2011.
- <a name="Zima12MSc"></a>Zima, M.,  
*[Hand/Arm Gesture Recognition based on Address-Event-Representation Data](http://repositum.tuwien.ac.at/obvutwhs/download/pdf/1623095?originalFilename=true)*,  
MSc. Thesis, TU Wien & AIT, Austria, 2012.
- <a name="Huber14MSc"></a>Huber, B.,  
*[High-Speed Pose Estimation using a Dynamic Vision Sensor](http://www.kutter-fonds.ethz.ch/App_Themes/default/datalinks/BasilHuber_UniZ_MT2014.pdf)*,  
MSc. Thesis, University of Zurich, Switzerland, 2014.
- <a name="Horstschaefer16MSc"></a>Horstschaefer, T.,  
*[Parallel Tracking, Depth Estimation, and Image Reconstruction with an Event Camera](http://www.kutter-fonds.ethz.ch/App_Themes/default/datalinks/2016_MT_Horstschaefer1.pdf)*,  
MSc. Thesis, University of Zurich, Switzerland, 2016.
- <a name="Kaelber16BS"></a>Kaelber, F., (Everding, L., Conradt, J.,)    
*[A probabilistic method for event stream registration](https://mediatum.ub.tum.de/doc/1327653/1327653.pdf)*,  
Bacherlor Thesis, TU Munich, Germany, 2016.
- <a name="Galanis16BS"></a>Galanis, M., (Everding, L., Conradt, J.,)    
*[DVS event stream registration](https://mediatum.ub.tum.de/doc/1367575/1367575.pdf)*,  
Bacherlor Thesis, TU Munich, Germany, 2016.
- <a name="ParedesValles18MSc"></a>Paredes-Valles, F.,  
*[Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation](http://resolver.tudelft.nl/uuid:aa13959b-79b9-4dfc-b5e0-7c501d9d3e2f)*,  
MSc. Thesis, TU Delft, The Netherlands, 2018.
- <a name="Nelson19MSc"></a>Nelson, K. J.,  
*[Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle](https://scholar.afit.edu/etd/2276/)*,  
MSc. Thesis, Air Force Institute of Technology, USA, 2019. [PDF](https://apps.dtic.mil/docs/citations/AD1076268), [PDF](https://apps.dtic.mil/dtic/tr/fulltext/u2/1076268.pdf)
- <a name="Attanasio19MSc"></a>Attanasio, G.,  
*[Event-based camera communications: a measurement-based analysis](https://webthesis.biblio.polito.it/11693/1/tesi.pdf)*,  
MSc. Thesis, Politecnico di Torino, Italy, 2019.
- <a name="Wang19MSc"></a>Wang, Z.,  
*[Motion Equivariance of Event-based Camera Data with the Temporal Normalization Transform](https://arxiv.org/pdf/1911.12801)*,  
MSc. Thesis, University of Pennsylvania, USA, 2019.
- <a name="Boettiger20MSc"></a>Boettiger, J. P.,  
*[A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors](https://scholar.afit.edu/etd/3154 )*,  
MSc. Thesis, Air Force Institute of Technology, USA, 2020. [PDF](https://scholar.afit.edu/cgi/viewcontent.cgi?article=4155&context=etd)
- <a name="Friedel20MSc"></a>Friedel, Z. P.,  
*[Event-Based Visual-Inertial Odometry Using Smart Features](https://scholar.afit.edu/etd/3172/)*,  
MSc. Thesis, Air Force Institute of Technology, USA, 2020.
- <a name="Verecken20MSc"></a>Verecken, J.,  
*[Embedded real-time inference in spiking neural networks for neuromorphic IoT vision sensors](http://hdl.handle.net/2078.1/thesis:26663)*,  
MSc. Thesis, Ecole polytechnique de Louvain, Université catholique de Louvain, Belgium, 2020.
- <a name="Gava20MSc"></a>Gava, L.,  
*[Event-driven Motion-In-Depth for Low Latency Control of the Humanoid Robot iCub](https://unire.unige.it/handle/123456789/3258)*,  
MSc. Thesis, University of Genoa, Italy, 2020.
- <a name="Dubeau22MSc"></a>Dubeau, E.,  
*[Suivi d'objet en 6 degrés de liberté avec caméra événementielle](http://hdl.handle.net/20.500.11794/100283)* (Object Tracking in 6-DOF using an event camera),  
MSc. Thesis, Université Laval, Canada, 2022.


<a name="people"></a>
# People / Organizations
- [Institute of NeuroInformatics](https://www.ini.uzh.ch/) (INI) of the University of Zurich (UZH) and ETH Zurich, Switzerland.
    - [INI Sensors Group](https://sensors.ini.ch)
- [iniVation AG](https://www.inivation.com/) (commercialization of neuromorphic vision technology from INI), Switzerland.
- [Western Sydney University’s International Centre for Neuromorphic Systems (ICNS)](https://www.westernsydney.edu.au/icns), Australia.
- [GRASP Lab](https://www.grasp.upenn.edu/) at University of Pennsylvania, [Kostas Daniilidis](http://www.cis.upenn.edu/~kostas/).
- [Robotics and Perception Group](http://rpg.ifi.uzh.ch/research_dvs.html) of the University of Zurich (UZH) and ETH Zurich, Switzerland.
- [Robotic Interactive Perception](https://sites.google.com/view/guillermogallego/research/event-based-vision) of TU Berlin, Germany.
- [Perception and Robotics Group](http://prg.cs.umd.edu/) at University of Maryland (UMD). [Fermüller's Lab on Event-based vision](http://users.umiacs.umd.edu/~fer/dvs.html)
- [Event-Driven Perception for Robotics (EDPR)](https://www.edpr.iit.it) group at Istituto Italiano di Tecnologia (IIT), Italy.
- [Computer Vision and Robotics Lab](https://www.ugr.es/~cvrlab/) of University of Granada, Spain.
- [Institut de la Vision](https://github.com/neuromorphic-paris) Neuromorphics group Paris, France.
- [AIT Austrian Institute of Technology](https://www.ait.ac.at/en/research-fields/new-sensor-technologies/) Sensing & vision solutions group in Vienna, Austria.
- [Sinapse](http://sinapse.nus.edu.sg/) Singapore Institute for Neurotechnology, Singapore.
- [Intel Labs](https://www.intel.com/content/www/us/en/research/overview.html), [Mike Davies](https://newsroom.intel.com/news/bobbleheads-tool-test-neuromorphic-mind/#gs.nndw9d) (Intel’s neuromorphic computing program leader).
  - [Video CVPRW 2019](https://youtu.be/jhQgElvtb1s)
  - [Video Intel NICE 2018 Loihi](https://vimeo.com/264342847),  [Video Intel NICE 2018 Day 3](https://vimeo.com/258246382)
  - [Advancing neuromorphic computing from lab to mainstream applications -Telluride 2020](https://youtu.be/bmCMsWF_ORg)
- [Robotics and Technology of Computers Lab - Sevilla (RTC)](http://www.rtc.us.es/) of the University of Seville (USE), Seville, Spain.
- [IMSE-CNM – Seville Institute of Microelectronics](http://imse-cnm.csic.es), Seville, Spain. [News](https://elpais.com/tecnologia/2020-04-13/chips-que-detectan-olores-o-como-la-tecnologia-intenta-imitar-el-funcionamiento-del-cerebro.html)
- [Prophesee SA](https://prophesee.ai/): Sensor and Software development and production.
- [Adaptive Robotic Controls Lab (ArcLab)](https://arclab.hku.hk/) of the University of Hong Kong (HKU).
- [Neuromorphic Automation and Intelligence Lab (NAIL)](https://sites.google.com/view/zhouyi-joey/home) at the Hunan University, China.
- [Mobile Perception Lab (MPL)](https://mpl.sist.shanghaitech.edu.cn/) at ShanghaiTech University, China.
- [Camera Intelligence Lab](https://ci.idm.pku.edu.cn/) at Peking University (PKU), China.
- [Nano(neuro)electronics Research Laboratory](https://engineering.purdue.edu/NRL) at Purdue University, USA.
- [Martinet Lab](https://www.i3s.unice.fr/jmartinet/) at Université Côte d'Azur, France.
- [Hyper Vision Research Laboratory](http://www.hvrl.ics.keio.ac.jp/) at Keio University, Japan.
- [Aoki Media Sensing Lab](https://aoki-medialab.jp/home-en/) at Keio University, Japan.
- [Visual Intelligence Lab.](http://vi.kaist.ac.kr/) at KAIST, South Korea.
- [Neuromorphic Computing Group (NCG)](https://www.zhaw.ch/en/lsfm/institutes-centres/icls/cognitive-computing-in-life-sciences/research-group-neuromorphic-computing/) at ZHAW Zurich University of Applied Sciences, Switzerland.
- [NeuroPAC](https://www.neuropac.info/)


<a name="press-eetimes"></a>
# Press EETimes
- [Neuromorphic Revolution to Start in 2024](https://www.eetimes.eu/neuromorphic-revolution-to-start-in-2024/), 10.2019.
- [Neuromorphic Vision Sensors Eye the Future of Autonomy](https://www.eetimes.com/neuromorphic-vision-sensors-eye-the-future-of-autonomy/), 04.2020.
- [Neuromorphic Vision Sensors Bring Autonomy Closer to Reality](https://www.eetasia.com/neuromorphic-vision-sensors-bring-autonomy-closer-to-reality/), 04.2020.
- [The Slow But Steady Rise of the Event Camera](https://www.eetimes.eu/the-slow-but-steady-rise-of-the-event-camera/), 06.2020
- [Europe Still the Focal Point for Neuromorphic Vision](https://www.eetimes.eu/europe-still-the-focal-point-for-neuromorphic-vision/), 07.2020.
- [Telluride Neuromorphic Engineering Workshop Goes Large](https://www.eetimes.com/telluride-neuromorphic-engineering-workshop-goes-large/), 07.2020.
- [Prophesee Touts Toolkit for Event-based Vision](https://www.eetimes.eu/prophesee-touts-toolkit-for-event-based-vision/), 09.2020.
- [Neuromorphic Vision in Space](https://www.eetimes.eu/neuromorphic-vision-in-space/), 07.2021. 
- [Prophesee Showcases Neuromorphic Vision Systems from Biotech to Space Debris](https://www.eetimes.eu/prophesee-showcases-neuromorphic-vision-systems-from-biotech-to-space-debris/), 12.2021.
- [What Does “Neuromorphic” Mean Today?](https://www.eetimes.com/what-does-neuromorphic-mean-today/), EETimes Special Issue, 07.2022.
    - [Exclusive: An Interview with Carver Mead](https://www.eetimes.com/exclusive-an-interview-with-carver-mead/), 07.2022.
    - [Inspiration or Imitation: How Closely Should We Copy Biological Systems?](https://www.eetimes.com/inspiration-or-imitation-how-closely-should-we-copy-biological-systems/), 07.2022.
    - [A Shift in Computer Vision is Coming](https://www.eetimes.com/a-shift-in-computer-vision-is-coming/), 04.2022.
    - [Neuromorphic Sensing: Coming Soon to Consumer Products](https://www.eetimes.com/neuromorphic-sensing-coming-soon-to-consumer-products/), 07.2022.    
    - [Reverse-Engineering Insect Brains to Make Robots](https://www.eetimes.com/reverse-engineering-insect-brains-to-make-robots/), 07.2022.
    - [Cars That Think Like You](https://www.eetimes.com/cars-that-think-like-you/), 07.2022.
- [Embedded AI Processors: The Cambrian Explosion](https://www.eetimes.eu/embedded-ai-processors-the-cambrian-explosion/)
- [EU-Funded NimbleAI to Deliver 3D Neuromorphic Chip](https://www.eetimes.eu/eu-funded-nimbleai-to-deliver-3d-neuromorphic-chip/), 04.2023.
- **[Brains and Machine Podcast](https://brainsandmachines.net/)**, since 09.2023. A podcast about neuromorphic engineering and bio-inspired technology.

# Press
- [Event-Based Vision: Taking a Cue From Biology](https://www.qualitymag.com/articles/96413-event-based-vision-taking-a-cue-from-biology), 03.2021.
- [Silicon retinas to help robots navigate the world](https://www.advancedsciencenews.com/silicon-retinas-to-help-robots-navigate-the-world/), Advanced Science News, 10.2022.
- [Event cameras at TU Berlin - SCIoI](https://www.scienceofintelligence.de/news-tu-press-release-puts-sciois-event-cameras-in-the-spotlight/?utm_source=CleverReach&utm_medium=email&utm_campaign=04-04-22+Name+of+Campaign&utm_content=Mailing_14489706), 06.2023.


<br><br>
<a name="contributing"></a>
# Contributing
Please see [CONTRIBUTING](https://github.com/uzh-rpg/event-based_vision_resources/blob/master/Contributing.md) for details.
***