Training neural geometry fields requires an NVIDIA GPU with CUDA support;
the memory usage of the training routine is modest, being able to run on a
3060 mobile GPU. The necessary python packages are listed in requirements.txt
so installing them with pip install -r requirements.txt is simplest.
Then run python source/train.py on any target mesh:
usage: train.py [-h] [--mesh MESH] [--lod LOD] [--features FEATURES] [--display DISPLAY] [--batch BATCH] [--fixed-seed]
options:
-h, --help show this help message and exit
--mesh MESH Target mesh
--lod LOD Number of patches to partition
--features FEATURES Feature vector size
--display DISPLAY Display the result after training
--batch BATCH Batch size for training
--fixed-seed Fixed random seed (for debugging)
The results of the training will be placed into a local results directory as follows:
results
├── binaries (Binaries for trained neural geometry fields)
├── loss (Loss plots)
├── meta (Generic metadata)
├── quadrangulated (Partitioned surfaces)
├── stl (Final surfaces exported as STLs)
└── torched (Pytorch binary data)
Source code for the real-time rasterizer is provided in the rasterizer directory. The only dependencies for building the program are GLFW and Vulkan; the rest (ImGui and glm) are provided as submodules of this project. We rely on CMake to compile the program:
cmake -B build .
cmake --build build -j
To run the rasterizer, run the resulting build/testbed executable by providing a path to the neural geometry field binary file (e.g. within results/binaries):
./build/testbed results/binaries/nefertiti-lod1000-f20.bin
@inproceedings{vs2024ngfs,
title = {Neural Geometry Fields for Meshes},
author = {Sivaram, Venkataram and Ramamoorthi, Ravi and Li, Tzu-Mao},
numpages = {11},
year = {2024},
series = {SIGGRAPH '24}
}