Tengfei Wang, Zongqian Zhan*, Rui Xia, Linxia Ji, Xin Wang*.
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/
# tetra-nerf for triangulation
cd submodules/tetra-triangulation
conda install cmake
conda install conda-forge::gmp
conda install conda-forge::cgal
cmake .
# you can specify your own cuda path
# export CPATH=/usr/local/cuda-11.3/targets/x86_64-linux/include:$CPATH
make
pip install -e .
The cropped Barn.ply is here
First, obtain the building mask, multi_mask, and white images, then place these three folders in the same directory as the image folder.
python train.py -s TNT_GOF/TrainingSet/Barn -m exp_TNT/Barn -r 2 --use_decoupled_appearance
python extract_mesh.py -m exp_TNT/Barn --iteration 30000
# Acknowledgements
This project is built upon [3DGS](https://github.com/graphdeco-inria/gaussian-splatting) and [GOF]([https://github.com/autonomousvision/mip-splatting](https://github.com/autonomousvision/gaussian-opacity-fields)).[2DGS]([https://github.com/hbb1/2d-gaussian-splatting])
# Citation