Zehao Yu · Torsten Sattler · Andreas Geiger
Paper | arXiv | Project Page
Gaussian Opacity Fields (GOF) enables geometry extraction with 3D Gaussians directly by indentifying its level set. Our regularization improves surface reconstruction and we utilize Marching Tetrahedra for compact and adaptive mesh extraction.
Clone the repository and create an anaconda environment using
git clone git@github.com:autonomousvision/gaussian-opacity-fields.git
cd gaussian-opacity-fields
conda create -y -n gof python=3.8
conda activate gof
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
conda install cudatoolkit-dev=11.3 -c conda-forge
pip install -r requirements.txt
pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn/
Please download the data from the Mip-NeRF 360 and request the authors for the treehill and flowers scenes.
This project is built upon 3DGS and Mip-Splatting. Regularizations are taken from 2DGS. Tetrahedra triangulation is taken from Tetra-NeRF. Marching Tetrahdedra is adapted from Kaolin Library. We thank all the authors for their great work and repos.
If you find our code or paper useful, please cite
@article{Yu2024GOF,
author = {Yu, Zehao and Sattler, Torsten and Geiger, Andreas},
title = {Gaussian Opacity Fields: Efficient High-quality Compact Surface Reconstruction in Unbounded Scenes},
journal = {arXiv},
year = {2024},
}If you find the regularizations useful, please kindly cite
@article{Huang2DGS2024,
title={2D Gaussian Splatting for Geometrically Accurate Radiance Fields},
author={Huang, Binbin and Yu, Zehao and Chen, Anpei and Geiger, Andreas and Gao, Shenghua},
journal={arXiv preprint arXiv:2403.17888},
year={2024}
}