base on [CVPR'24 Best Student Paper] Mip-Splatting: Alias-free 3D Gaussian Splatting <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)}, 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}, } ``` ", Assign "at most 3 tags" to the expected json: {"id":"5452","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"