# mip-splatting

**Repository Path**: week233/mip-splatting

## Basic Information

- **Project Name**: mip-splatting
- **Description**: [CVPR'24 Best Student Paper] Mip-Splatting: Alias-free 3D Gaussian Splatting
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-11
- **Last Updated**: 2025-03-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">

  <h1 align="center">Mip-Splatting: Alias-free 3D Gaussian Splatting</h1>
  <p align="center">
    <a href="https://niujinshuchong.github.io/">Zehao Yu</a>
    ·
    <a href="https://apchenstu.github.io/">Anpei Chen</a>
    ·
    <a href="https://github.com/hbb1">Binbin Huang</a>
    ·
    <a href="https://tsattler.github.io/">Torsten Sattler</a>
    ·
    <a href="http://www.cvlibs.net/">Andreas Geiger</a>

  </p>
  <h2 align="center">CVPR 2024 Best Student Paper</h2>

  <h3 align="center"><a href="https://drive.google.com/file/d/1Q7KgGbynzcIEyFJV1I17HgrYz6xrOwRJ/view?usp=sharing">Paper</a> | <a href="https://arxiv.org/pdf/2311.16493.pdf">arXiv</a> | <a href="https://niujinshuchong.github.io/mip-splatting/">Project Page</a>  | <a href="https://niujinshuchong.github.io/mip-splatting-demo/">Online Viewer</a> </h3>
  <div align="center"></div>
</p>


<p align="center">
  <a href="">
    <img src="./media/bicycle_3dgs_vs_ours.gif" alt="Logo" width="95%">
  </a>
</p>

<p align="center">
We introduce a 3D smoothing filter and a 2D Mip filter for 3D Gaussian Splatting (3DGS), eliminating multiple artifacts and achieving alias-free renderings.  
</p>
<br>

# Update
We integrated an improved densification metric proposed in [Gaussian Opacity Fields](https://niujinshuchong.github.io/gaussian-opacity-fields/), which significantly improves the novel view synthesis results, please check the [paper](https://arxiv.org/pdf/2404.10772.pdf) for details. Please download the lastest code and reinstall `diff-gaussian-rasterization` to try it out. 

# Installation
Clone the repository and create an anaconda environment using
```
git clone git@github.com: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/
```

# Dataset
## Blender Dataset
Please download and unzip nerf_synthetic.zip from the [NeRF's official Google Drive](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1). Then generate multi-scale blender dataset with
```
python convert_blender_data.py --blender_dir nerf_synthetic/ --out_dir multi-scale
```

## Mip-NeRF 360 Dataset
Please download the data from the [Mip-NeRF 360](https://jonbarron.info/mipnerf360/) and request the authors for the treehill and flowers scenes.

# Training and Evaluation
```
# single-scale training and multi-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 
```

# Online viewer
After training, you can fuse the 3D smoothing filter to the Gaussian parameters with
```
python create_fused_ply.py -m {model_dir}/{scene} --output_ply fused/{scene}_fused.ply"
```
Then use our [online viewer](https://niujinshuchong.github.io/mip-splatting-demo) to visualize the trained model.

# Acknowledgements
This project is built upon [3DGS](https://github.com/graphdeco-inria/gaussian-splatting). Please follow the license of 3DGS. We thank all the authors for their great work and repos. 

# Citation
If you find our code or paper useful, please cite
```bibtex
@InProceedings{Yu2024MipSplatting,
    author    = {Yu, Zehao and Chen, Anpei and Huang, Binbin and Sattler, Torsten and Geiger, Andreas},
    title     = {Mip-Splatting: Alias-free 3D Gaussian Splatting},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {19447-19456}
}
```
If you find our improved densification metric useful, please kindly 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:2404.10772},
  year      = {2024},
}
```