{ "cells": [ { "cell_type": "markdown", "id": "f53b58d7-4a4c-41cd-b689-c42462d7c464", "metadata": {}, "source": [ "# Volumetric inverse rendering" ] }, { "cell_type": "markdown", "id": "2c00d2cc-3e22-464c-bf13-cf703e29cf23", "metadata": {}, "source": [ "## Overview\n", "\n", "In this tutorial, we use Mitsuba's differentiable volumetric path tracer to optimize a scattering volume to match a set of (synthetic) reference images. We will optimize a 3D volume density that's stored on a regular grid. The optimization will account for both direct and indirect illumination by using [path replay backpropagation][1] to compute derivatives of delta tracking and volumetric multiple scattering. The reconstructed volume parameters can then for example be re-rendered using novel illumination conditions.\n", "\n", "\n", "
VolumeGrid][2] object in conjunction with [TensorXf][3]. The `VolumeGrid` class is responsible for loading and writing volumes from disk, similar to the `Bitmap` class for images. Using the `grid` property of the [gridvolume][4] plugin, it is possible to pass it directly to the plugin constructor in Python.\n",
"\n",
"We initialize the extinction `sigma_t` to a low constant value, (e.g. `0.002`). This tends to help the optimization process, as it seems to be easier for the optimizer to increase the volume density rather than remove parts of a very dense volume. \n",
"\n",
"Note that we use a fairly small initial volume resolution here. This is done on purpose since we will upsample the volume grid during the actual optimization process. As explained later, this typically improves the convexity of the volume optimization problem.\n",
"\n",
"[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_media.html#heterogeneous-medium-heterogeneous\n",
"[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.VolumeGrid\n",
"[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.TensorXf\n",
"[4]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_media.html#grid-based-volume-data-source-gridvolume"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "82313a97-a314-43a3-8a4b-f4e32305f1a2",
"metadata": {},
"outputs": [],
"source": [
"v_res = 16\n",
"\n",
"# Modify the scene dictionary\n",
"scene_dict['object'] = {\n",
" 'type': 'cube',\n",
" 'interior': {\n",
" 'type': 'heterogeneous',\n",
" 'sigma_t': {\n",
" 'type': 'gridvolume',\n",
" 'grid': mi.VolumeGrid(dr.full(mi.TensorXf, 0.002, (v_res, v_res, v_res, 1))),\n",
" 'to_world': T().translate(-1).scale(2.0)\n",
" },\n",
" 'scale': 40.0,\n",
" },\n",
" 'bsdf': {'type': 'null'}\n",
"}\n",
"\n",
"scene = mi.load_dict(scene_dict)"
]
},
{
"cell_type": "markdown",
"id": "5890a714-a81c-4f37-bc9f-7ec2e4335a6a",
"metadata": {},
"source": [
"We load the modified scene and render it for all view angles. Those are going to be our initial image in the optimization process."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "91050ab4-91ac-413b-89c0-959d024498e0",
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
"dr.upsample()][1], a functions for up-sampling tensor and texture data. We can easily create a higher resolution volume by passing the current optimzed tensor and specifying the desired shape (must be powers of two when upsampling `TensorXf`).\n",
"\n",
"[1]: https://drjit.readthedocs.io/en/latest/reference.html#drjit.upsample"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bbdfe9de-d1b2-4d06-89ed-ce2d93d1c383",
"metadata": {},
"outputs": [],
"source": [
"opt[key] = dr.upsample(opt[key], shape=(64, 64, 64))\n",
"params.update(opt);"
]
},
{
"cell_type": "markdown",
"id": "068b99e7-fb72-4b3f-b166-2d7930daf86d",
"metadata": {},
"source": [
"Rendering the new, upsampled volume we can already notice a slight difference in the apparent sharpness. This is due to the *trilinear* interpolation of density values that is used by the volumetric path tracer."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "352c5176-ded0-4d13-8996-5d4dbe006c3b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"prbvolpath plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_integrators.html#path-replay-backpropagation-volumetric-integrator-prbvolpath)\n",
"- [heterogeneous plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_media.html#heterogeneous-medium-heterogeneous)\n",
"- [gridvolume plugin](https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_media.html#grid-based-volume-data-source-gridvolume)\n",
"- [mitsuba.VolumeGrid](https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.VolumeGrid)\n",
"- [mitsuba.TensorXf](https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.TensorXf)\n",
"- [dr.upsample](https://drjit.readthedocs.io/en/latest/reference.html#drjit.upsample)"
]
}
],
"metadata": {
"interpreter": {
"hash": "afd680236861e4ad68138f9ddf1f8bff806918beb77b7f0c16179efa24869fce"
},
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}