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[CVPR'24 Best Student Paper] Mip-Splatting: Alias-free 3D Gaussian Splatting
| Date | Stars |
|---|---|
| 2026-07-24 | 1459 |
| 2026-07-25 | 1460 |
| 2026-07-28 | 1460 |
| 2026-07-30 | 1460 |
| 2026-08-06 | 1460 |
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<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 [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/
```
# 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)},
monthExcerpt of 4,418 characters
Read on GitHub
14
YAN Wenkun · China
1
1
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matched fp:7f99bd244bb646ba, topic:nerf, topic:3d-reconstruction, readme:computer vision