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.
For differentiable marching tetrahedons, we have adapted code from NVIDIA’s Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research.
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This work is made available under the Nvidia Source Code License.
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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 (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 11.3
conda create -n dmodel python=3.9 activate dmodel conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch 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
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
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.
spot.json– Extracting a 3D model of the spot model. Geometry, materials, and lighting from image observations.
spot_fixlight.json– Same as above but assuming known environment lighting.
spot_metal.json– Example of joint learning of materials and high frequency environment lighting to showcase split-sum.
bob.json– Simple example of a genus 1 model.
We additionally include configs (
nerd_*.json) to reproduce the main results of the paper. We rely on third party datasets, which
are courtesy of their respective authors. Please note
that individual licenses apply to each dataset. To automatically download and pre-process all datasets, run the
activate dmodel cd data python download_datasets.py
Below follows more information and instructions on how to manually install the datasets (in case the automated script fails).
NeRF synthetic dataset Our view interpolation results use the synthetic dataset from the original NeRF paper.
To manually install it, download the NeRF synthetic dataset archive
and unzip it into the
nvdiffrec/data folder. This is required for running any of the
NeRD dataset We use datasets from the NeRD paper, which features real-world photogrammetry and inaccurate
(manually annotated) segmentation masks. Clone the NeRD datasets using git and rescale them to 512 x 512 pixels resolution using the script
scale_images.py. This is required for running any of the
activate dmodel cd nvdiffrec/data/nerd git clone https://github.com/vork/ethiopianHead.git git clone https://github.com/vork/moldGoldCape.git python scale_images.py
Server usage (through Docker)
- Build docker image.
cd docker ./make_image.sh nvdiffrec:v1
Start an interactive docker container:
docker run --gpus device=0 -it --rm -v /raid:/raid -it nvdiffrec:v1 bash
docker run --gpus device=1 -d -v /raid:/raid -w=[path to the code] nvdiffrec:v1 python train.py --config configs/bob.json