{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Granular phase function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "This tutorial showcases a more practical application and is a great introduction to write efficient integrator-like rendering code with the Mitsuba library.\n", "\n", "In volumetric rendering, the *phase function* describes the angular distribution of light scattering when interacting with a particle in the medium. Have you ever wondered how a participating medium would look like if every particle had the shape of a bunny, or was made of frosty glass? ❄️🐰 For this you would need to know the phase function of such a medium, and this is what we are going to compute in this tutorial.\n", "\n", "The following code is inspired from this [paper][1], where the authors manage to efficiently render granular materials, like sand, snow or sugar.\n", "\n", "At a very high-level, here are the key stages of the algorithm we are going to use in this tutorial:\n", "\n", "- Represent a single particle with a shape and a BSDF\n", "- Generate rays coming from random directions around the particle object\n", "- Compute bounces of the light path inside the particle until they escape\n", "- Once escaped, record the out-going direction in the local frame of the original direction into a histogram\n", "\n", "
\n", "\n", "🚀 **You will learn how to:**\n", "\n", "\n", "\n", "
\n", "\n", "[1]: https://cs.dartmouth.edu/wjarosz/publications/meng15granular.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As always, we start by importing the libraries and setting the variant." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import drjit as dr\n", "import mitsuba as mi\n", "\n", "mi.set_variant('llvm_ad_rgb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializating the scene \n", "\n", "To keep this tutorial simple, we are only going to compute the phase function of a dielectric sphere. Although only minor changes would be necessary to allow the use of other shapes and materials in this script (e.g., a frosty glass bunny ❄️🐰).\n", "\n", "On top of various mesh loaders (e.g., `ply`, `obj`, ...), Mitsuba supports several analytical shapes (e.g., `Sphere`, `Rectangle`, ...), that can be very handy when writing prototype applications.\n", "\n", "As done previously in other tutorials, we are going to use the [load_dict()][1] routine to instanciate a scene containing a sphere and a dielectric BSDF.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.load_dict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "scene = mi.load_dict({\n", " 'type' : 'scene',\n", " 'grain' : {\n", " 'type' : 'sphere',\n", " }\n", "})\n", "\n", "bsdf = mi.load_dict({\n", " 'type' : 'dielectric',\n", " 'int_ior' : 'water', # We can also use float values\n", " 'ext_ior' : 'air',\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Performing Monte-Carlo integration requires the ability to generate random numbers. Mitsuba comes with a set of [Sampler][1] classes that can be used to do exactly that. For simplicity, in this application, we are going to use the most basic sampler, [independent][2], which we can instanciate with the [load_dict()][3] routine.\n", "\n", "In order to write vectorized code, we need to choose a *wavefront* size, corresponding to the number of light paths we are going to compute simultaneously. The `sampler` instance needs to be aware of the wavefront size so to produce random arrays of the right size. This can be done using the [Sampler.seed()][4] method at the same time as choosing a seed for our random number generator (here `0`).\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler\n", "[2]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_samplers.html#independent-sampler-independent\n", "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.load_dict\n", "[4]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler.seed" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "sampler = mi.load_dict({'type' : 'independent'})\n", "sampler.seed(0, wavefront_size=int(1e7))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating primary rays\n", "\n", "In the following cell, we are going to generate our primary rays, coming from all directions towards the center of the scene. For this, we use the sampler instance to generate random ray directions and offsets for the ray origins. The `Sampler` class only implements methods for producing 1D/2D uniformly distributed points in the unit interval/square. Fortunately, Mitsuba provides a whole set of warping techniques that map from the unit square to other domains such as spheres, hemispheres, etc. Here we leverage the `warp.square_to_uniform_sphere()` routine to generate a set of random ray directions from the 2D unit points given using [Sampler.next_2d()][1].\n", "\n", "We then construct a coordinate frame [Frame3f][2] around the sampled ray directions which we will use to convert between local and world coordinate spaces.\n", "\n", "We sample ray origin positions by first computing 2D offsets in $[-1, 1]^2$ using the sampler instance. We can then easily compute 3D positions in local coordinates, where we offset the z-component by `-1` as the z-axis represents the forward ray direction. Using the coordinate frame object, we convert the ray origin positions from local to world coordinate space.\n", "\n", "The [Scene][3] class exposes a [bbox()][4] method that can be used to retrieve the bounding box of the entire scene. We then convert it to a bounding sphere and use it to move the ray origin accordingly so to make sure our set of rays covers the entire scene domain.\n", "\n", "Finally, we create primary rays of type [Ray3f][5], setting the desired wavelength for our simulation.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler.next_2d\n", "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Frame3f\n", "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene\n", "[4]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene.bbox\n", "[5]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Ray3f" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Sample ray directions\n", "d = mi.warp.square_to_uniform_sphere(sampler.next_2d())\n", "\n", "# Construct coordinate frame object\n", "frame = mi.Frame3f(d)\n", "\n", "# Sample ray origins\n", "xy_local = 2.0 * sampler.next_2d() - 1.0\n", "local_o = mi.Vector3f(xy_local.x, xy_local.y, -1.0)\n", "world_o = frame.to_world(local_o)\n", "\n", "# Move ray origin according to scene bounding sphere\n", "bsphere = scene.bbox().bounding_sphere()\n", "o = world_o * bsphere.radius + bsphere.center\n", "\n", "# Construct rays\n", "rays = mi.Ray3f(o, d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intra-grain transport\n", "\n", "Let's now use those rays to construct light paths bouncing many times inside of the particle object.\n", "\n", "For this, we perform a first ray intersection query with the scene using [Scene.ray_intersect()][1] which will return a [SurfaceInteraction3f][2] object, containing the surface interaction information. We use [is_valid()][2] to find the rays that actually interact with the object.\n", "\n", "After initializing the `throughput` and `active` variables, we perform a symbolic loop to compute the different bounces of the light paths. At every iteration of the loop. we compute the following:\n", "\n", "- Sample new directions from the BSDF using the sampler instance and the current surface interaction\n", "- Update the throughput and rays for the next bounce using the [SurfaceInteraction3f.spawn_ray()][3] method\n", "- Trace the new set of rays to find the next intersection with the object\n", "- Evaluate the kernels to make sure the JIT compiler doesn't accumulate instructions of all loop iterations\n", "\n", "We wrap this computation in the Python function sample, where the Dr.Jit decorator [@dr.syntax][4] provides the syntactic sugar that allows us to express the symbolic control flow statements [dr.while_loop][5] and [dr.if_stmt][6] within the function as standard Python while and if statements respectively.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene.ray_intersect\n", "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.SurfaceInteraction3f\n", "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.SurfaceInteraction3f.spawn_ray\n", "[4]: https://drjit.readthedocs.io/en/latest/cflow.html#control-flow\n", "[5]: https://drjit.readthedocs.io/en/latest/reference.html#drjit.while_loop\n", "[6]: https://drjit.readthedocs.io/en/latest/reference.html#drjit.if_stmt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "@dr.syntax \n", "def sample(rays: mi.Ray3f, sampler: mi.Sampler):\n", "\n", " # Find first ray intersection with the object\n", " si = scene.ray_intersect(rays)\n", " valid = si.is_valid()\n", "\n", " # Maximum number of bounces\n", " max_bounces = 10\n", "\n", " # Loop state variables\n", " throughput = mi.Spectrum(1.0)\n", " active = mi.Bool(valid)\n", " i = mi.UInt32(0)\n", " \n", " while active & (i < max_bounces):\n", " # Sample new direction\n", " ctx = mi.BSDFContext()\n", " bs, bsdf_val = bsdf.sample(ctx, si, sampler.next_1d(), sampler.next_2d(), active)\n", "\n", " # Update throughput and rays for next bounce\n", " throughput[active] *= bsdf_val\n", " rays[active] = si.spawn_ray(si.to_world(bs.wo))\n", "\n", " # Find next intersection\n", " si = scene.ray_intersect(rays, active)\n", " active &= si.is_valid()\n", " \n", " # Increase loop iteration counter\n", " i += 1\n", "\n", " # Only account for rays that have escaped\n", " valid &= ~active\n", " \n", " # We don't care about a specific color for this tutorial\n", " return rays, mi.luminance(throughput), valid\n", " \n", "rays, throughput, valid = sample(rays, sampler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the histogram\n", "\n", "We are only interested in the light paths that have escaped the object, hence we first need to update the `valid` array mask.\n", "\n", "The escaping directions are given by the final ray direction, which we convert to local coordinates using the previously built `frame` object. It is then straighforward to compute the `theta` angle in spherical coordinates and the corresponding bin index in the histrogram.\n", "\n", "Here it is important to account for the distortion introduced in the projection to spherical coordinates, hence we divide the throughput variable with the projection jacobian.\n", "\n", "The histogram array is initialized using `dr.zero` and we accumulate the computed values into their corresponding bins using the `dr.scatter_reduce(dr.ReduceOp.Add, ...)` routine." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Resolution of the histogram\n", "histogram_size = 512\n", "\n", "# Convert escaping directions into histogram bin indices\n", "cos_theta = mi.Frame3f.cos_theta(frame.to_local(rays.d))\n", "theta = dr.acos(cos_theta)\n", "theta_idx = mi.UInt32(theta / dr.pi * histogram_size)\n", "\n", "# Account for projection jacobian\n", "throughput *= 1.0 / dr.sqrt(1 - cos_theta**2)\n", "\n", "# Accumulate values into the histogram\n", "histogram = dr.zeros(mi.Float, histogram_size)\n", "dr.scatter_reduce(dr.ReduceOp.Add, histogram, throughput, theta_idx, valid)\n", "\n", "# Execute the kernel by evaluating the histogram\n", "dr.eval(histogram);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the histogram\n", "\n", "Let's now take a look at the resulting angular distribution! We plot it in log scale on a regular plot as well as a polar plot." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbsphinx-thumbnail": {}, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "x = dr.linspace(mi.Float, 0, dr.pi, len(histogram), endpoint=True)\n", "histogram_log = np.array(dr.log(histogram))\n", "\n", "fig = plt.figure(figsize = (10, 5))\n", "\n", "ax = fig.add_subplot(121, title='Angular distribution') \n", "ax.plot(np.array(dr.cos(x)), histogram_log, color='C0')\n", "ax.set_xlabel(r'$\\cos(\\theta)$', size=15)\n", "ax.set_ylabel(r'$p(\\cos(\\theta))$', size=15)\n", "\n", "ax = fig.add_subplot(122, polar=True, title='polar plot') \n", "ax.plot(np.array(x), histogram_log, color='C0')\n", "ax.plot(np.array(-x), histogram_log, color='C0', label='test');" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## See also\n", "\n", "- [The independent sampler][1]\n", "- [mitsuba.warp.square_to_uniform_sphere()][2]\n", "- [mitsuba.Ray3f][3]\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_samplers.html#independent-sampler-independent\n", "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.warp.sqaure_to_uniform_sphere\n", "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Ray3f" ] } ], "metadata": { "file_extension": ".py", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "mimetype": "text/x-python", "name": "python", "npconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 3 }, "nbformat": 4, "nbformat_minor": 4 }