Zehao Yu · Anpei Chen · Binbin Huang · Torsten Sattler · Andreas Geiger
Paper | arXiv | Project Page
We introduce a 3D smoothing filter and a 2D Mip filter for 3D Gaussian Splatting (3DGS), eliminating multiple artifacts and achieving alias-free renderings.
Clone the repository and create an anaconda environment using
git clone [email protected]:autonomousvision/mip-splatting.git
cd mip-splatting
conda create -y -n mip-splatting python=3.8
conda activate mip-splatting
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 and unzip nerf_synthetic.zip from the NeRF's official Google Drive. Then generate multi-scale blender dataset with
python convert_blender_data.py --blender_dir nerf_synthetic/ --out_dir multi-scale
Please download the data from the Mip-NeRF 360 and request the authors for the treehill and flowers scenes.
# single-scale training and single-scale testing on NeRF-synthetic dataset
python scripts/run_nerf_synthetic_stmt.py
# multi-scale training and multi-scale testing on NeRF-synthetic dataset
python scripts/run_nerf_synthetic_mtmt.py
# single-scale training and single-scale testing on the mip-nerf 360 dataset
python scripts/run_mipnerf360.py
# single-scale training and multi-scale testing on the mip-nerf 360 dataset
python scripts/run_mipnerf360_stmt.py
This project is built upon 3DGS. Please follow the license of 3DGS. We thank all the authors for their great work and repos.
If you find our code or paper useful, please cite
@article{Yu2023MipSplatting,
author = {Yu, Zehao and Chen, Anpei and Huang, Binbin and Sattler, Torsten and Geiger, Andreas},
title = {Mip-Splatting: Alias-free 3D Gaussian Splatting},
journal = {arXiv:2311.16493},
year = {2023},
}