{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Projective sampling integrators\n", "===============================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview \n", "\n", "In this tutorial, we will optimize a curve such that its shadows match some target reference. We will do this by only observing the shadows and making use of the the projective sampling integrators.\n", "\n", "Gradients introduced by indirect visibilty effects, such as shadows or reflections are difficult to capture. In previous tutorials, we mentioned how discontinuities require special care and that Mitsuba 3 uses a \"projective sampling\"-based method to handle these. Here, we will dig deeper into the integrators that implement this method:\n", "\n", "- [direct_projective][1]\n", "- [prb_projective][2]\n", "\n", "More details about this projective sampling method can be found in the paper \"[Projective Sampling for Differentiable Rendering of Geometry][3]\".\n", "\n", "
\n", "\n", "🚀 **You will learn how to:**\n", " \n", "\n", " \n", "
\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#direct-illumination-projective-sampling-direct-projective\n", "[2]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#projective-sampling-path-replay-backpropagation-prb-prb-projective\n", "[3]: http://rgl.epfl.ch/publications/Zhang2023Projective" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As always, let's start by importing `drjit` and `mitsuba` and set a differentiation-aware variant." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import drjit as dr\n", "import mitsuba as mi\n", "import numpy as np\n", "import os\n", "\n", "mi.set_variant('cuda_ad_rgb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scene\n", "\n", "We want to cast two distinct shadows from a single object and have the shadows form specific shapes. More specifically, we'll be optimizing a 3D curve using the [bsplinecurve][1] plugin such that its two shadows \"draw\" a moon and a star.\n", "\n", "We provide you with a pre-built scene in a XML file, we just need to load it. Let's also render it.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_shapes.html#b-spline-curve-bsplinecurve" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = rgb,\n", " component_format = float32,\n", " size = [256, 128],\n", " srgb_gamma = 0,\n", " struct = Struct<12>[\n", " float32 R; // @0, premultiplied alpha\n", " float32 G; // @4, premultiplied alpha\n", " float32 B; // @8, premultiplied alpha\n", " ],\n", " data = [ 384 KiB of image data ]\n", "]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scene = mi.load_file('../scenes/shadow_art.xml')\n", "image_scene = mi.render(scene, spp=4096)\n", "mi.Bitmap(image_scene)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the bottom, you can see our curve shape that we've wrapped on itself to create a circle. In the top left and top right, there are the two shadows that were casted by the curve onto two separate walls. We can't see the emitters from this point of view, as they are both slightly behind the sensor's viewpoint." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of this tutorial, we will only be considering the shadows themselves during the optimization. We should therefore build two sensors that respectively look straight at the left and right wall. Neither sensor should see the curve directly. For convenience, Mitsuba ships with a [batch][1] sensor, which renders multiple views at once. There is a performance benefit to this approach: the just-in-time compiler only needs to trace the rendering process once instead of multiple times (once for each viewpoint).\n", "\n", "With the [batch][1] sensor, the rendered output is a single image of all viewpoints stitched together horizontally.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_sensors.html#batch-sensor-batch" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = rgb,\n", " component_format = float32,\n", " size = [256, 128],\n", " srgb_gamma = 0,\n", " struct = Struct<12>[\n", " float32 R; // @0, premultiplied alpha\n", " float32 G; // @4, premultiplied alpha\n", " float32 B; // @8, premultiplied alpha\n", " ],\n", " data = [ 384 KiB of image data ]\n", "]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist = 5\n", "batch_sensor = mi.load_dict({\n", " 'type': 'batch',\n", " 'sensor1': {\n", " 'type': 'perspective',\n", " 'fov': 100,\n", " 'to_world': mi.ScalarTransform4f.look_at(\n", " origin=[0, 0, -dist + 1],\n", " target=[0, 0, -dist],\n", " up=[0, 1, 0]\n", " ),\n", " },\n", " 'sensor2': {\n", " 'type': 'perspective',\n", " 'fov': 100,\n", " 'to_world': mi.ScalarTransform4f.look_at(\n", " origin=[-dist + 1, 0, 0],\n", " target=[-dist, 0, 0],\n", " up=[0, 1, 0]\n", " ),\n", " },\n", " 'film': {\n", " 'type': 'hdrfilm',\n", " 'width': 128 * 2,\n", " 'height': 128,\n", " 'sample_border': True,\n", " 'filter': { 'type': 'box' }\n", " },\n", " 'sampler': {\n", " 'type': 'independent',\n", " 'sample_count': 256\n", " }\n", "})\n", "\n", "image_primal = mi.render(scene, sensor=batch_sensor, spp=4096)\n", "mi.Bitmap(image_primal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reference image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now load the reference image (by [Ziyi Zhang][1]). As mentionned previously, our goal is to reconstruct a star and moon outline with the shadows. Keep in mind that although the image shows both outlines next to each other, they will be on separate walls in our scene.\n", "\n", "Note that if you want use your own reference image, you should carefully match the pixel values of the initial images (wall and shadow values).\n", "\n", "[1]: http://rgl.epfl.ch/people/ziyi" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = y,\n", " component_format = float32,\n", " size = [256, 128],\n", " srgb_gamma = 0,\n", " struct = Struct<4>[\n", " float32 Y; // @0, premultiplied alpha\n", " ],\n", " metadata = {\n", " generatedBy => \"Mitsuba version 3.4.0\",\n", " pixelAspectRatio => 1,\n", " screenWindowWidth => 1\n", " },\n", " data = [ 128 KiB of image data ]\n", "]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bitmap_ref = mi.Bitmap('../scenes/references/starmoon.exr')\n", "image_ref = mi.TensorXf(bitmap_ref)\n", "\n", "# Reshape into [height, widht, 1]\n", "resolution = bitmap_ref.size()\n", "image_ref = mi.TensorXf(image_ref.array, shape=[resolution[1], resolution[0], 1])\n", "\n", "bitmap_ref" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projective sampling integrator\n", "\n", "In this section, we will go over some of the options/parameters that are availble in the projective sampling integrators. We will see how they impact the quality of the gradients." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finite differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We assess the quality of our gradients by comparing them against a ground truth measurement using finite differences. As was mentioned in previous tutorials, visualizing forward gradients is a nice method to get a better understanding of how some change in a parameter will change the rendered output.\n", "\n", "To capture a forward-mode gradient image, we must apply some change to our scene. Here, we arbitraily chose to look at a rotation aound the Y axis (vertical axis) of the [bsplinecurve][1] object\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_shapes.html#b-spline-curve-bsplinecurve" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "key = 'curve.control_points'\n", "params = mi.traverse(scene)\n", "initial_control_points = mi.Float(params[key])\n", "\n", "def apply_y_rotation(params, value):\n", " control_points = dr.unravel(mi.Point4f, params[key])\n", " radii = control_points[3]\n", " points = mi.Point3f(control_points.x, control_points.y, control_points.z)\n", " \n", " rotation = mi.Transform4f.rotate([0, 1, 0], value)\n", " rotated_points = rotation @ points\n", " new_control_points = mi.Point4f(rotated_points.x, rotated_points.y, rotated_points.z, radii)\n", " \n", " params[key] = dr.ravel(new_control_points)\n", " params.update()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We render the finite differences gradient estimate in multiple rounds with different seeds to avoid exceeding the maximum number of samples in a single kernel. In total we are using approximately 4.3 billion samples." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32\r" ] } ], "source": [ "epsilon = 1e-3\n", "fd_spp = 2048\n", "fd_repeat = 32 if 'PYTEST_CURRENT_TEST' not in os.environ else 1\n", "res = batch_sensor.film().size()\n", "img1 = dr.zeros(mi.TensorXf, (res[1], res[0], 3))\n", "img2 = dr.zeros(mi.TensorXf, (res[1], res[0], 3))\n", "\n", "for it in range(fd_repeat):\n", " apply_y_rotation(params, -epsilon)\n", " img1 += mi.render(scene, sensor=batch_sensor, spp=fd_spp, seed=it)\n", " params[key] = initial_control_points # Undo translation\n", " params.update()\n", " \n", " apply_y_rotation(params, +epsilon)\n", " img2 += mi.render(scene, sensor=batch_sensor, spp=fd_spp, seed=it)\n", " params[key] = initial_control_points # Undo translation\n", " params.update()\n", " \n", " print(f\"{it+1}/{fd_repeat}\", end='\\r')\n", " \n", "img_fd = (img2 - img1) / (epsilon*2) / fd_repeat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now visualize the sum of all 3 channels' gradients. In the plot below, warmer colors indicate an increase in pixel value and colder values a decrease.\n", "\n", "We can see how the shadow on the left is becoming thinner, whereas the one on the right is becoming larger. This is expected as the circle will become more perpendicular to the wall in one case and more parallel in the other." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import matplotlib.cm as cm\n", "\n", "vlim = dr.max(dr.abs(img_fd))[0] * 1\n", "plt.imshow(np.sum(img_fd, axis=2), cmap=cm.coolwarm, vmin=-vlim, vmax=vlim)\n", "plt.title(\"Gradient image using finite differences\")\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `direct_projective` integrator\n", "\n", "The [direct_projective][1] plugin has many parameters which allow us to fine tune its performance and use cases:\n", "\n", "* `sppc`: number of samples per pixel used to estimate the continuous derivatives\n", "* `sppp`: number of samples per pixel used to estimate the gradients resulting from primary visibility changes\n", "* `sppi`: number of samples per pixel used to estimate the gradients resulting from indirect visibility changes\n", "* `guiding`: the type of guiding method used to estimate the indirect visibility derivatives \n", "* `guiding_proj`: whether or not to use projective sampling to build the guiding structure\n", "\n", "Internally, the integrator separates the dervivative computation into 3 separate integrals: one for each derivative kind. The `sppc`, `sppp`, `sppi` values control the number of samples used for each one of these estimates. In addition, the indirect visibility changes can be challenging to capture, so the integrator makes use of a guiding data structure. This guiding structure can be initialized/constructed by using projected samples. We won't go into the details of this process here, and invite you to read more about it in the paper \"[Projective Sampling for Differentiable Rendering of Geometry][2]\" if this is of interest to you.\n", "\n", "The optimization problem we are considering in this task is very specific, in that it only has indirectly visibile discontinuous gradients. This is due to the fact that our sensor is **only** looking at the shadows of our changing curve. We can pass this information to the integrator, by setting the sppc and sppp to 0. The benefit of doing this is fairly obvious: the integrator has two less integrals to estimate and therefore should run faster.\n", "\n", "As its name suggests, the `direct_projective` integrator can only handle direct illumination. Mitsuba also ships with a [prb_projective][3] integrator, which can handle longer lights paths whilst still using the projective sampling method.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#direct-illumination-projective-sampling-direct-projective\n", "[2]: http://rgl.epfl.ch/publications/Zhang2023Projective\n", "[3]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#projective-sampling-path-replay-backpropagation-prb-prb-projective" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the `direct_projective` integrator will use an octree as its guiding structure and will have projective sampling enabled. Let's use that to compute a forward-mode gradient image." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "integrator = mi.load_dict({\n", " 'type': 'direct_projective',\n", " 'sppc': 0,\n", " 'sppp': 0,\n", " 'sppi': 1024,\n", "})\n", "\n", "theta = mi.Float(0.0)\n", "dr.enable_grad(theta)\n", "apply_y_rotation(params, theta) \n", "dr.forward(theta, dr.ADFlag.ClearEdges) # Forward to scene parameters\n", "\n", "image = mi.render(scene, params=params, sensor=batch_sensor, integrator=integrator)\n", "img_guided = dr.forward_to(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For comparison purposes, let's try not using any guiding structure at all." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "integrator = mi.load_dict({\n", " 'type': 'direct_projective',\n", " 'sppc' : 0,\n", " 'sppp' : 0,\n", " 'sppi' : 1024,\n", " 'guiding' : 'none'\n", "})\n", "\n", "# Compute the forward gradient\n", "theta = mi.Float(0.0)\n", "dr.enable_grad(theta)\n", "apply_y_rotation(params, theta) \n", "dr.forward(theta, dr.ADFlag.ClearEdges) # Forward to scene parameters\n", "\n", "image = mi.render(scene, params=params, sensor=batch_sensor, integrator=integrator)\n", "img_unguided = dr.forward_to(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now visualize all three of our gradient images.\n", "\n", "First of all, the unguided derivative estimate is very far from the ground truth. This speaks to the difficulty of sampling these lights paths which introduce indirect visbility discontinuities. Secondly, even by using a fraction of the total number of samples used in the finite differences estimate, the guided derivative is close to the ground truth and visually smoother." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import matplotlib.cm as cm\n", "\n", "fig, axx = plt.subplots(1, 3, figsize=(18, 8))\n", "vlim = dr.max(dr.abs(img_fd))[0] * 1\n", "images = [img_fd, img_guided, img_unguided]\n", "titles = [\"Finite differences\", \"Guided projective sampling\", \"Unguided\"]\n", "for i, ax in enumerate(axx):\n", " ax.imshow(np.sum(images[i], axis=2), cmap=cm.coolwarm, vmin=-vlim, vmax=vlim)\n", " ax.set_title(titles[i])\n", " ax.axis('off')\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous section is not only meant to be a small educational introduction to the projective sampling integrators, it also allows us to establish good hyperparameters for our integrator. This can be critical in certain optimization problems as the quality of the gradients has a direct influence on the outcome of the task." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Reset the circle to its initial position before starting the optimization.\n", "params[key] = initial_control_points\n", "params.update();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can finally run our optimization! We don't introduce any new concepts here, with maybe the exception that we carefully remove the control points' radii from the set of differentiable parameters. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "450/450 loss: 0.001637\r" ] } ], "source": [ "integrator = mi.load_dict({\n", " 'type': 'direct_projective',\n", " 'sppc': 0,\n", " 'sppp': 0,\n", " 'sppi': 1024,\n", "})\n", "\n", "radii = dr.unravel(mi.Point4f, initial_control_points)[3]\n", "opt = mi.ad.Adam(lr=1e-2)\n", "opt[key] = params[key]\n", "\n", "max_iter = 450 if 'PYTEST_CURRENT_TEST' not in os.environ else 5\n", "for it in range(max_iter):\n", " control_points = dr.unravel(mi.Point4f, opt[key])\n", " control_points[3] = radii # Do not optimize the radius\n", " params[key] = dr.ravel(control_points)\n", " params.update()\n", "\n", " img = mi.render(scene, params=params, sensor=batch_sensor, integrator=integrator, seed=it)\n", "\n", " # L2 loss\n", " loss = dr.mean(dr.sqr(img - image_ref))\n", " dr.backward(loss)\n", "\n", " # Take a gradient step\n", " opt.step()\n", "\n", " # Log\n", " print(f\"{it+1}/{max_iter} loss: {loss[0]:.6f}\", end='\\r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can have a look at the final state of our scene, and see how the curve was stretched and twisted into a seemingly random shape whilst still producing shadows matching our reference." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = rgb,\n", " component_format = float32,\n", " size = [256, 128],\n", " srgb_gamma = 0,\n", " struct = Struct<12>[\n", " float32 R; // @0, premultiplied alpha\n", " float32 G; // @4, premultiplied alpha\n", " float32 B; // @8, premultiplied alpha\n", " ],\n", " data = [ 384 KiB of image data ]\n", "]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_scene = mi.render(scene, spp=4096)\n", "mi.Bitmap(image_scene)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also have a closer look at the shadows. They don't match perfectly - this problem is fairly ambiguous and without any sort of regularization it is quite easy to make \"knots\" in the curve which are hard to recover from.\n", "\n", "The point of this is tutorial is to focus on projective integrators -- if you want to play with shadow art with your own reference images, it is recommended to add gradient preconditioning to the pipeline, add regularization to curve smoothness, and gradually give the curve more freedom to move around. This will help avoid getting stuck in local minima." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "nbsphinx-thumbnail": {}, "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "image_final = mi.render(scene, sensor=batch_sensor, spp=4096)\n", "mi.Bitmap(image_final)\n", "\n", "fig, axx = plt.subplots(1, 2, figsize=(18, 8))\n", "images = [image_ref, image_final]\n", "titles = [\"Reference\", \"Optimized shadows\"]\n", "for i, ax in enumerate(axx):\n", " ax.imshow(mi.util.convert_to_bitmap(images[i]))\n", " ax.set_title(titles[i], fontsize=12)\n", " ax.axis('off')\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## See also\n", "\n", "- [batch plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_sensors.html#batch-sensor-batch)\n", "- [direct_projective plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#direct-illumination-projective-sampling-direct-projective)\n", "- [prb_projective plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#projective-sampling-path-replay-backpropagation-prb-prb-projective)" ] } ], "metadata": { "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": "3.9.12" }, "vscode": { "interpreter": { "hash": "0a71e52312f80bab873d5923c8fb89fb81086c5728dcd0bc2f504667ecab6c4e" } } }, "nbformat": 4, "nbformat_minor": 4 }