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":39,"name":"3d-generation","display_name":"3D generation","slug":"3d-generation"},{"id":3,"name":"ai-agent","display_name":"AI agent","slug":"ai-agent"},{"id":8,"name":"ai-coding","display_name":"AI coding assistant","slug":"ai-coding"},{"id":5,"name":"ai-image","display_name":"AI image generation","slug":"ai-image"},{"id":9,"name":"ai-infrastructure","display_name":"AI infrastructure","slug":"ai-infrastructure"},{"id":10,"name":"ai-memory","display_name":"AI memory","slug":"ai-memory"},{"id":11,"name":"ai-skills","display_name":"AI skills","slug":"ai-skills"},{"id":12,"name":"ai-translation","display_name":"AI translation","slug":"ai-translation"},{"id":6,"name":"ai-video","display_name":"AI video generation","slug":"ai-video"},{"id":4,"name":"ai-voice","display_name":"AI voice","slug":"ai-voice"},{"id":7,"name":"ai-workflow","display_name":"AI workflow","slug":"ai-workflow"},{"id":22,"name":"audio-processing","display_name":"Audio processing","slug":"audio-processing"},{"id":29,"name":"authentication","display_name":"Authentication","slug":"authentication"},{"id":51,"name":"bundler","display_name":"Bundler","slug":"bundler"},{"id":41,"name":"chatbot","display_name":"Chatbot","slug":"chatbot"},{"id":27,"name":"cloud-native","display_name":"Cloud native","slug":"cloud-native"},{"id":1,"name":"computer-vision","display_name":"Computer vision","slug":"computer-vision"},{"id":37,"name":"crypto-trading","display_name":"Crypto trading","slug":"crypto-trading"},{"id":57,"name":"curated-list","display_name":"Curated list","slug":"curated-list"},{"id":54,"name":"data-streaming","display_name":"Data streaming","slug":"data-streaming"},{"id":35,"name":"data-visualization","display_name":"Data visualization","slug":"data-visualization"},{"id":16,"name":"database-backup","display_name":"Database backup","slug":"database-backup"},{"id":49,"name":"design-system","display_name":"Design system","slug":"design-system"},{"id":38,"name":"digital-human","display_name":"Digital human","slug":"digital-human"},{"id":34,"name":"document-processing","display_name":"Document processing","slug":"document-processing"},{"id":44,"name":"ecommerce","display_name":"E-commerce","slug":"ecommerce"},{"id":45,"name":"emulator","display_name":"Emulator","slug":"emulator"},{"id":46,"name":"file-management","display_name":"File management","slug":"file-management"},{"id":32,"name":"fintech","display_name":"Fintech","slug":"fintech"},{"id":31,"name":"game-development","display_name":"Game development","slug":"game-development"},{"id":24,"name":"headless-browser","display_name":"Headless browser","slug":"headless-browser"},{"id":52,"name":"headless-cms","display_name":"Headless CMS","slug":"headless-cms"},{"id":36,"name":"home-automation","display_name":"Home automation","slug":"home-automation"},{"id":20,"name":"image-editing","display_name":"Image editing","slug":"image-editing"},{"id":28,"name":"iot","display_name":"IoT","slug":"iot"},{"id":13,"name":"local-llm","display_name":"Local LLM","slug":"local-llm"},{"id":17,"name":"mcp","display_name":"MCP","slug":"mcp"},{"id":47,"name":"monitoring","display_name":"Monitoring","slug":"monitoring"},{"id":2,"name":"nlp","display_name":"NLP","slug":"nlp"},{"id":26,"name":"observability","display_name":"Observability","slug":"observability"},{"id":40,"name":"pentesting","display_name":"Pentesting","slug":"pentesting"},{"id":48,"name":"programming-examples","display_name":"Programming examples","slug":"programming-examples"},{"id":42,"name":"proxy","display_name":"Proxy","slug":"proxy"},{"id":14,"name":"rag","display_name":"RAG","slug":"rag"},{"id":56,"name":"resume-building","display_name":"Resume building","slug":"resume-building"},{"id":33,"name":"robotics","display_name":"Robotics","slug":"robotics"},{"id":30,"name":"search","display_name":"Search","slug":"search"},{"id":43,"name":"self-hosted","display_name":"Self-hosted","slug":"self-hosted"},{"id":50,"name":"static-analysis","display_name":"Static analysis","slug":"static-analysis"},{"id":18,"name":"synthetic-data","display_name":"Synthetic data","slug":"synthetic-data"},{"id":19,"name":"text-to-speech","display_name":"Text to speech","slug":"text-to-speech"},{"id":53,"name":"ui-components","display_name":"UI components","slug":"ui-components"},{"id":15,"name":"vector-database","display_name":"Vector database","slug":"vector-database"},{"id":21,"name":"video-editing","display_name":"Video editing","slug":"video-editing"},{"id":25,"name":"web-scraping","display_name":"Web scraping","slug":"web-scraping"},{"id":55,"name":"webassembly","display_name":"WebAssembly","slug":"webassembly"},{"id":23,"name":"workflow-automation","display_name":"Workflow automation","slug":"workflow-automation"}]" returns me the "expected json"