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Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".
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# nvdiffrec
Joint optimization of topology, materials and lighting from multi-view image observations
as described in the paper
[Extracting Triangular 3D Models, Materials, and Lighting From Images](https://nvlabs.github.io/nvdiffrec/).
For differentiable marching tetrahedons, we have adapted code from NVIDIA's [Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research](https://github.com/NVIDIAGameWorks/kaolin).
# News
- **2023-10-20** : We added a version of the renderutils library written in [slangpy](https://shader-slang.com/slang/user-guide/a1-02-slangpy.html) to leverage the autodiff capabilities of slang instead of CUDA extensions with manually crafted forward and backward passes. This simplifies the code substantially, with the same runtime performance as before. This version is available in the `slang` [branch](https://github.com/NVlabs/nvdiffrec/tree/slang) of this repo.
- **2023-09-15** : We added support for the [FlexiCubes](https://research.nvidia.com/labs/toronto-ai/flexicubes/) isosurfacing technique. Please see the config `configs/bob_flexi.json` for a usage example, and refer to the [FlexiCubes documentation](https://github.com/nv-tlabs/FlexiCubes) for details.
# Citation
```
@inproceedings{Munkberg_2022_CVPR,
author = {Munkberg, Jacob and Hasselgren, Jon and Shen, Tianchang and Gao, Jun and Chen, Wenzheng
and Evans, Alex and M\"uller, Thomas and Fidler, Sanja},
title = "{Extracting Triangular 3D Models, Materials, and Lighting From Images}",
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {8280-8290}
}
```
# Licenses
Copyright © 2022, NVIDIA Corporation. All rights reserved.
This work is made available under the [Nvidia Source Code License](https://github.com/NVlabs/nvdiffrec/blob/main/LICENSE.txt).
For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/).
# Installation
Requires Python 3.6+, VS2019+, Cuda 11.3+ and PyTorch 1.10+
Tested in Anaconda3 with Python 3.9 and PyTorch 1.10
## One time setup (Windows)
Install the [Cuda toolkit](https://developer.nvidia.com/cuda-toolkit) (required to build the PyTorch extensions).
We support Cuda 11.3 and above.
Pick the appropriate version of PyTorch compatible with the installed Cuda toolkit.
Below is an example with Cuda 11.6
```
conda create -n dmodel python=3.9
activate dmodel
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
pip install ninja imageio PyOpenGL glfw xatlas gdown
pip install git+https://github.com/NVlabs/nvdiffrast/
pip install --global-option="--no-networks" git+https://github.com/NVlabs/tiny-cuda-nn#subdirectory=bindings/torch
imageio_download_bin freeimage
```
### Every new command prompt
`activate dmodel`
# Examples
*Our approach is designed for high-end NVIDIA GPUs with large amounts of memory.
To run on mid-range GPU's, reduce the batch size parameter in the .json files.*
Simple genus 1 reconstruction example:
```
python train.py --config configs/bob.json
```
Visualize training progress (only supported on Windows):
```
python train.py --config configs/bob.json --display-interval 20
```
Multi GPU example (Linux only. *Experimental: all results in the paper were generated using a single GPU*),
using [PyTorch DDP](https://pytorch.org/docs/stable/elastic/run.html#launcher-api)
```
torchrun --nproc_per_node=4 train.py --config configs/bob.json
```
Below, we show the starting point and the final result. References to the right.
The results will be stored in the `out` folder.
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