{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Object pose estimation" ] }, { "cell_type": "markdown", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "## Overview\n", "\n", "In this tutorial, we will show how to optimize the pose of an object while correctly accounting for the visibility discontinuities. We are going to optimize several latent variables that control the translation and rotation of the object.\n", "\n", "In differentiable rendering, we aim to evaluate the derivative of a pixel intensity integral with respect to a scene parameter $\\pi$ as follows:\n", "\n", "\\begin{equation}\n", "\\partial_\\pi I(\\pi) = \\partial_\\pi \\int_P f(\\textbf{x}, \\pi) ~ d\\textbf{x}\n", "\\end{equation}\n", "\n", "where $\\textbf{x}$ is a light path in the path space $P$.\n", "\n", "When the function $f(\\cdot)$ is continuous w.r.t. $\\pi$, we can move the derivative into the integral and then apply Monte Carlo integration. Under this assumption, differentiating the rendering process via automatic differentiation, as in the previous tutorials, is correct.\n", "\n", "However, if $f(\\cdot)$ has discontinuities w.r.t. $\\pi$, direct application of automatic differentiation is not correct anymore, as it omits an integral term given by the [Reynolds transport theorem](https://en.wikipedia.org/wiki/Reynolds_transport_theorem). This needs to be considered when differentiating shape-related parameters (e.g., position), as the discontinuities in the visiblity function (the silhouette of the object) are then dependent on the differentiated parameter.\n", "\n", "In the last years, several works tried to address this issue (e.g., Li et al. (2018), Zhang et al. (2020), Loubet et al. (2019), Bangaru et al. (2020), Zhang et al. (2023), ...). Mitsuba provides dedicated integrators implementing the \"*projective sampling*\"-based approach (Zhang et al. (2023)).\n", "\n", "- [direct_projective][1]: projective sampling direct illumination integrator\n", "- [prb_projective][2]: projective sampling wth Path Replay Backpropagation (PRB) integrator\n", "\n", "In this tutorial, we will optimize the position and rotation of a mesh in order to match a target rendering. To keep things simple, we will use the `direct_projective` integrator.\n", "You will learn more about this integrator in the following tutorials.\n", "\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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As always, let's import `drjit` and `mitsuba` and set a differentiation-aware variant." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import drjit as dr\n", "import mitsuba as mi\n", "\n", "mi.set_variant('cuda_ad_rgb', 'llvm_ad_rgb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `direct_projective` and scene construction\n", "\n", "We will rely on the `direct_projective` integrator for this tutorial to properly handle the visibility discontinuities in our differentiable simulation. In primal rendering, this integrator is identical to the `direct` integrator." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "integrator = {\n", " 'type': 'direct_projective',\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create a simple scene with a bunny placed in front of a gray wall, illuminated by a spherical light." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from mitsuba.scalar_rgb import Transform4f as T\n", "\n", "scene = mi.load_dict({\n", " 'type': 'scene',\n", " 'integrator': integrator,\n", " 'sensor': {\n", " 'type': 'perspective',\n", " 'to_world': T().look_at(\n", " origin=(0, 0, 2),\n", " target=(0, 0, 0),\n", " up=(0, 1, 0)\n", " ),\n", " 'fov': 60,\n", " 'film': {\n", " 'type': 'hdrfilm',\n", " 'width': 64,\n", " 'height': 64,\n", " 'rfilter': { 'type': 'gaussian' },\n", " 'sample_border': True\n", " },\n", " },\n", " 'wall': {\n", " 'type': 'obj',\n", " 'filename': '../scenes/meshes/rectangle.obj',\n", " 'to_world': T().translate([0, 0, -2]).scale(2.0),\n", " 'face_normals': True,\n", " 'bsdf': {\n", " 'type': 'diffuse',\n", " 'reflectance': { 'type': 'rgb', 'value': (0.5, 0.5, 0.5) },\n", " }\n", " },\n", " 'bunny': {\n", " 'type': 'ply',\n", " 'filename': '../scenes/meshes/bunny.ply',\n", " 'to_world': T().scale(6.5),\n", " 'bsdf': {\n", " 'type': 'diffuse',\n", " 'reflectance': { 'type': 'rgb', 'value': (0.3, 0.3, 0.75) },\n", " },\n", " },\n", " 'light': {\n", " 'type': 'obj',\n", " 'filename': '../scenes/meshes/sphere.obj',\n", " 'emitter': {\n", " 'type': 'area',\n", " 'radiance': {'type': 'rgb', 'value': [1e3, 1e3, 1e3]}\n", " },\n", " 'to_world': T().translate([2.5, 2.5, 7.0]).scale(0.25)\n", " }\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reference image\n", "\n", "Next we generate the target rendering. We will later modify the bunny's position and rotation to set the initial optimization state." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = rgb,\n", " component_format = uint8,\n", " size = [64, 64],\n", " srgb_gamma = 1,\n", " struct = Struct<3>[\n", " uint8 R; // @0, normalized, gamma, premultiplied alpha\n", " uint8 G; // @1, normalized, gamma, premultiplied alpha\n", " uint8 B; // @2, normalized, gamma, premultiplied alpha\n", " ],\n", " data = [ 12 KiB of image data ]\n", "]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img_ref = mi.render(scene, seed=0, spp=1024)\n", "\n", "mi.util.convert_to_bitmap(img_ref)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizer and latent variables\n", "\n", "As done in previous tutorial, we access the scene parameters using the `traverse()` mechanism. We then store a copy of the initial vertex positions. Those will be used later to compute the new vertex positions at every iteration, always applying a different transformation on the same base shape. \n", "\n", "Since the vertex positions in `Mesh` are stored in a linear buffer (e.g., `x_1, y_1, z_1, x_2, y_2, z_2, ...`), we use the `dr.unravel()` routine to unflatten that array into a `Point3f` array." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "params = mi.traverse(scene)\n", "initial_vertex_positions = dr.unravel(mi.Point3f, params['bunny.vertex_positions'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While it would be possible to optimize the vertex positions of the bunny independently, in this example we are only going to optimize a translation and rotation parameter. This drastically constrains the optimization process, which helps with convergence.\n", "\n", "Therefore, we instantiate an optimizer and assign two variables to it: `angle` and `trans`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "opt = mi.ad.Adam(lr=0.025)\n", "opt['angle'] = mi.Float(0.25)\n", "opt['trans'] = mi.Point2f(0.1, -0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the optimizer's point of view, those variables are the same as any other variables optimized in the previous tutorials, to the exception that when calling `opt.update()`, the optimizer doesn't know how to propagate their new values to the scene parameters. This has to be done *manually*, and we encapsulate exactly that logic in the function defined below. More detailed explaination on this can be found [here][1].\n", "\n", "After clipping the optimized variables to a proper range, this function creates a transformation object combining a translation and rotation and applies it to the vertex positions stored previously. It then flattens those new vertex positions before assigning them to the scene parameters.\n", "\n", "[1]: https://mitsuba.readthedocs.io/en/latest/src/how_to_guides/use_optimizers.html#Optimizing-latent-variables" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def apply_transformation(params, opt):\n", " opt['trans'] = dr.clip(opt['trans'], -0.5, 0.5)\n", " opt['angle'] = dr.clip(opt['angle'], -0.5, 0.5)\n", " \n", " trafo = mi.Transform4f().translate([opt['trans'].x, opt['trans'].y, 0.0]).rotate([0, 1, 0], opt['angle'] * 100.0)\n", " \n", " params['bunny.vertex_positions'] = dr.ravel(trafo @ initial_vertex_positions)\n", " params.update()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is now time to apply our first transformation to get the bunny to its initial state before starting the optimization." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "Bitmap[\n", " pixel_format = rgb,\n", " component_format = uint8,\n", " size = [64, 64],\n", " srgb_gamma = 1,\n", " struct = Struct<3>[\n", " uint8 R; // @0, normalized, gamma, premultiplied alpha\n", " uint8 G; // @1, normalized, gamma, premultiplied alpha\n", " uint8 B; // @2, normalized, gamma, premultiplied alpha\n", " ],\n", " data = [ 12 KiB of image data ]\n", "]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "apply_transformation(params, opt)\n", "\n", "img_init = mi.render(scene, seed=0, spp=1024)\n", "\n", "mi.util.convert_to_bitmap(img_init)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following cell we define the hyper parameters controlling the optimization, such as the number of iterations and number of samples per pixels for the differentiable rendering simulation:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "iteration_count = 100\n", "spp = 16" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "nbsphinx": "hidden", "tags": [] }, "outputs": [], "source": [ "# IGNORE THIS: When running under pytest, adjust parameters to reduce computation time\n", "import os\n", "if 'PYTEST_CURRENT_TEST' in os.environ:\n", " iteration_count = 2\n", " spp = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimization loop below is very similar to the one used in the other tutorials, except that we need to apply the transformation to update the bunny's state and record the relation between the rendered image and the optimized parameters." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 99: error=0.00266705, angle=-0.0015, trans=[-0.0013, -0.0016]\r" ] } ], "source": [ "import time\n", "loss_hist = []\n", "for it in range(iteration_count):\n", " # Apply the mesh transformation\n", " apply_transformation(params, opt)\n", " \n", " # Perform a differentiable rendering\n", " img = mi.render(scene, params, seed=it, spp=spp)\n", "\n", " # Evaluate the objective function\n", " loss = dr.sum(dr.square(img - img_ref)) / len(img.array)\n", " \n", " # Backpropagate through the rendering process\n", " dr.backward(loss)\n", "\n", " # Optimizer: take a gradient descent step\n", " opt.step()\n", "\n", " loss_hist.append(loss.array[0])\n", " print(f\"Iteration {it:02d}: error={loss}, angle={opt['angle'][0]:.4f}, trans=[{opt['trans'].x[0]:.4f}, {opt['trans'].y[0]:.4f}]\", end='\\r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the results\n", "\n", "Finally, let's visualize the results and plot the loss over iterations" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "nbsphinx-thumbnail": {}, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n", "\n", "axs[0][0].plot(loss_hist)\n", "axs[0][0].set_xlabel('iteration'); \n", "axs[0][0].set_ylabel('Loss'); \n", "axs[0][0].set_title('Parameter error plot');\n", "\n", "axs[0][1].imshow(mi.util.convert_to_bitmap(img_init))\n", "axs[0][1].axis('off')\n", "axs[0][1].set_title('Initial Image')\n", "\n", "axs[1][0].imshow(mi.util.convert_to_bitmap(mi.render(scene, spp=1024)))\n", "axs[1][0].axis('off')\n", "axs[1][0].set_title('Optimized image')\n", "\n", "axs[1][1].imshow(mi.util.convert_to_bitmap(img_ref))\n", "axs[1][1].axis('off')\n", "axs[1][1].set_title('Reference Image');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## See also\n", "\n", "- [Detailed look at Optimizer](https://mitsuba.readthedocs.io/en/latest/src/how_to_guides/use_optimizers.html)\n", "- [mitsuba.ad.Optimizer](https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.ad.Optimizer)\n", "- [prb_projective plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#projective-sampling-path-replay-backpropagation-prb-prb-projective)\n", "- [direct_projective plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#direct-illumination-projective-sampling-direct-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": "3.10.4" }, "vscode": { "interpreter": { "hash": "524ef5c892e16adde62d9febd56e921248026795a369fc4d8cb46b781ffa4996" } } }, "nbformat": 4, "nbformat_minor": 4 }