{"id":31211504,"url":"https://github.com/cyberagentailab/supernormal","last_synced_at":"2025-09-21T05:30:14.061Z","repository":{"id":230133955,"uuid":"777526943","full_name":"CyberAgentAILab/SuperNormal","owner":"CyberAgentAILab","description":"[CVPR 2024] Official implementation of \"SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration\"","archived":false,"fork":false,"pushed_at":"2025-03-31T07:12:26.000Z","size":11085,"stargazers_count":186,"open_issues_count":5,"forks_count":7,"subscribers_count":10,"default_branch":"main","last_synced_at":"2025-09-10T07:42:52.530Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CyberAgentAILab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-03-26T02:30:12.000Z","updated_at":"2025-09-08T06:04:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"382cee06-dcd4-4001-a177-04934207fd18","html_url":"https://github.com/CyberAgentAILab/SuperNormal","commit_stats":null,"previous_names":["cyberagentailab/supernormal"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/CyberAgentAILab/SuperNormal","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberAgentAILab%2FSuperNormal","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberAgentAILab%2FSuperNormal/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberAgentAILab%2FSuperNormal/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberAgentAILab%2FSuperNormal/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CyberAgentAILab","download_url":"https://codeload.github.com/CyberAgentAILab/SuperNormal/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberAgentAILab%2FSuperNormal/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276195627,"owners_count":25601152,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-21T02:00:07.055Z","response_time":72,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-09-21T05:30:11.426Z","updated_at":"2025-09-21T05:30:14.046Z","avatar_url":"https://github.com/CyberAgentAILab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch2 align=\"center\"\u003eSuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration\u003c/h2\u003e\n\u003ch4 align=\"center\"\u003e\n    \u003ca href=\"https://xucao-42.github.io/homepage/\"\u003e\u003cstrong\u003eXu Cao\u003c/strong\u003e\u003c/a\u003e\n    ·\n    \u003ca href=\"https://taketomitakafumi.sakura.ne.jp/web/en/\"\u003e\u003cstrong\u003eTakafumi Taketomi\u003c/strong\u003e\u003c/a\u003e\n\u003cbr\u003e\nCyberAgent \u003c/h4\u003e\n\u003ch4 align=\"center\"\u003e\u003ca href=\"https://cvpr.thecvf.com/\"\u003eCVPR 2024 \u003c/a\u003e\u003c/h3\u003e\n\u003cp align=\"center\"\u003e\n  \u003cbr\u003e\n    \u003ca href=\"https://arxiv.org/abs/2312.04803\"\u003e\n      \u003cimg src='https://img.shields.io/badge/arXiv-Paper-981E32?style=for-the-badge\u0026Color=B31B1B' alt='arXiv PDF'\u003e\n    \u003c/a\u003e\n\n[//]: # (    \u003ca href='https://xucao-42.github.io/mvas_homepage/'\u003e)\n\n[//]: # (      \u003cimg src='https://img.shields.io/badge/MVAS-Project Page-5468FF?style=for-the-badge' alt='Project Page'\u003e\u003c/a\u003e)\n\u003c/p\u003e\n\n\n### Update\n- **2024/09/30**: Real-world raw data and step-by-step data pre-processing instructions are available. See [here](./data_capture_and_preprocessing/README.md).\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"./media/teaser.png\" alt=\"Teaser\" width=\"100%\"\u003e\nFast and fine-grained 3D reconstruction from multi-view surface normal maps. \n\u003c/div\u003e\n\n### Quick Start\nCode was tested on Ubuntu 18.04 (WSL2) using Python 3.8, PyTorch 2.1.0, and CUDA 11.8 on an Nvidia RTX4090Ti (24GB). \n\n**Before started, please ensure CUDA is installed in your environment ([11.8 can be found here](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux\u0026target_arch=x86_64\u0026Distribution=WSL-Ubuntu\u0026target_version=2.0\u0026target_type=deb_local)).**\nIt is required by [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn).\n\n\u003cdetails\u003e\u003csummary\u003e You should see something like the following after typing `nvcc --version` \u003c/summary\u003e\n\n```commandline\nnvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2022 NVIDIA Corporation\nBuilt on Wed_Sep_21_10:33:58_PDT_2022\nCuda compilation tools, release 11.8, V11.8.89\nBuild cuda_11.8.r11.8/compiler.31833905_0\n```\n\u003c/details\u003e\n\nClone the repository and prepare the conda environment:\n```commandline\ngit clone https://github.com/CyberAgentAILab/SuperNormal.git\ncd SuperNormal\n. ./create_env.sh\n```\n\nDownload data (~1.8GB):\n```commandline\n./download_data.sh\n```\n\nRun on the DiLiGenT-MV benchmark objects or on our captured objects:\n```commandline\n./run_diligent.sh  # Training should take about 50 seconds per object\n```\nor \n```commandline\n./run_own_object.sh  # Training should take about 5 minutes per object\n```\nResults are saved under `./exp`.\n\nNOTE: If RuntimeError like below occurs, `apt install ninja-build` may resolve the error.\n```\nRuntimeError: Ninja is required to load C++ extensions\n```\n\n### Hyperparameter tuning tips\nTraining hyperparameters are defined in `./configs/*.conf`.\nSome important hyperparameters are:\n- `dataset.normal_dir`: You can choose normal maps estimated by different methods as input for DiLiGenT-MV benchmark objects.\n- `train.end_iter`: The number of iterations for training. Should be adjusted according to the number of views and normal map resolutions.\n- `train.increase_bindwidth_every`: A strategy used in [Neuralangelo](https://research.nvidia.com/labs/dir/neuralangelo/) to progressively activate finer hash grid during training. Less than `end_iter`/`model.encoding.n_levels` should be fine.\n- `train.batch_size`: Number of patches in each batch for training. Should be adjusted according to the GPU memory.\n- `train.patch_size`: Better to be fixed to 3, i.e., each patch is 3x3. Large patch size will cause inaccurate volume rendering results for boudary pixels in a patch.\n\n### Modifications to NerfAcc\nWe add several functions to the original [NerfAcc](https://www.nerfacc.com) to adapt it to patch-based volume rendering.\nThe key new functions (which are indicated by `patch_based` in function name) are in \n[third_parties/nerfacc-0.3.5/nerfacc-0.3.5/nerfacc/cuda/csrc/render_weight.cu/](./third_parties/nerfacc-0.3.5/nerfacc-0.3.5/nerfacc/cuda/csrc/render_weight.cu) \nand [third_parties/nerfacc-0.3.5/nerfacc-0.3.5/nerfacc/vol_rendering.py](./third_parties/nerfacc-0.3.5/nerfacc-0.3.5/nerfacc/vol_rendering.py).\n\n\n### Acknowledgement\nThis repo is built up on [NeuS](https://github.com/Totoro97/NeuS) and benefits from the amazing [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn) and [NerfAcc](https://www.nerfacc.com).\nWe also learned a lot from [instant-nsr-pl](https://github.com/bennyguo/instant-nsr-pl).\n\n### Citation\nIf you find our work useful in your research, please consider citing:\n```bibtex\n@inproceedings{supernormal2024cao,\n  title={SuperNormal: {N}eural Surface Reconstruction via Multi-View Normal Integration},\n  author={Cao Xu and Taketomi Takafumi},\n  booktitle={CVPR},\n  year={2024}\n}\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyberagentailab%2Fsupernormal","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcyberagentailab%2Fsupernormal","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyberagentailab%2Fsupernormal/lists"}