{"id":13444142,"url":"https://github.com/facebookresearch/pytorch3d","last_synced_at":"2025-05-12T05:24:26.276Z","repository":{"id":36971472,"uuid":"217433767","full_name":"facebookresearch/pytorch3d","owner":"facebookresearch","description":"PyTorch3D is FAIR's library of reusable components for deep learning with 3D data","archived":false,"fork":false,"pushed_at":"2025-03-28T15:26:58.000Z","size":52174,"stargazers_count":9238,"open_issues_count":291,"forks_count":1366,"subscribers_count":150,"default_branch":"main","last_synced_at":"2025-05-12T02:41:08.582Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://pytorch3d.org/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/facebookresearch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":".github/CODE_OF_CONDUCT.md","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}},"created_at":"2019-10-25T02:23:45.000Z","updated_at":"2025-05-12T02:09:36.000Z","dependencies_parsed_at":"2024-12-30T16:40:23.766Z","dependency_job_id":"8bdefd3f-771a-4e63-876e-35e676d51559","html_url":"https://github.com/facebookresearch/pytorch3d","commit_stats":{"total_commits":1154,"total_committers":151,"mean_commits":7.642384105960265,"dds":0.6594454072790294,"last_synced_commit":"4ae25bfce7eb42042a34585acc3df81cf4be7d85"},"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fpytorch3d","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fpytorch3d/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fpytorch3d/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fpytorch3d/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/facebookresearch","download_url":"https://codeload.github.com/facebookresearch/pytorch3d/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253672698,"owners_count":21945480,"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","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":"2024-07-31T03:02:20.162Z","updated_at":"2025-05-12T05:24:26.247Z","avatar_url":"https://github.com/facebookresearch.png","language":"Python","readme":"\u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/pytorch3dlogo.png\" width=\"900\"/\u003e\n\n[![CircleCI](https://circleci.com/gh/facebookresearch/pytorch3d.svg?style=svg)](https://circleci.com/gh/facebookresearch/pytorch3d)\n[![Anaconda-Server Badge](https://anaconda.org/pytorch3d/pytorch3d/badges/version.svg)](https://anaconda.org/pytorch3d/pytorch3d)\n\n# Introduction\n\nPyTorch3D provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).\n\nKey features include:\n\n- Data structure for storing and manipulating triangle meshes\n- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)\n- A differentiable mesh renderer\n- Implicitron, see [its README](projects/implicitron_trainer), a framework for new-view synthesis via implicit representations. ([blog post](https://ai.facebook.com/blog/implicitron-a-new-modular-extensible-framework-for-neural-implicit-representations-in-pytorch3d/))\n\nPyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.\nFor this reason, all operators in PyTorch3D:\n\n- Are implemented using PyTorch tensors\n- Can handle minibatches of hetereogenous data\n- Can be differentiated\n- Can utilize GPUs for acceleration\n\nWithin FAIR, PyTorch3D has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739).\n\nSee our [blog post](https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/) to see more demos and learn about PyTorch3D.\n\n## Installation\n\nFor detailed instructions refer to [INSTALL.md](INSTALL.md).\n\n## License\n\nPyTorch3D is released under the [BSD License](LICENSE).\n\n## Tutorials\n\nGet started with PyTorch3D by trying one of the tutorial notebooks.\n\n|\u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/dolphin_deform.gif\" width=\"310\"/\u003e|\u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/bundle_adjust.gif\" width=\"310\"/\u003e|\n|:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------:|\n| [Deform a sphere mesh to dolphin](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/bundle_adjustment.ipynb) |\n\n| \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/render_textured_mesh.gif\" width=\"310\"/\u003e | \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/camera_position_teapot.gif\" width=\"310\" height=\"310\"/\u003e\n|:------------------------------------------------------------:|:--------------------------------------------------:|\n| [Render textured meshes](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb)| [Camera position optimization](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb)|\n\n| \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/pointcloud_render.png\" width=\"310\"/\u003e | \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/cow_deform.gif\" width=\"310\" height=\"310\"/\u003e\n|:------------------------------------------------------------:|:--------------------------------------------------:|\n| [Render textured pointclouds](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_colored_points.ipynb)| [Fit a mesh with texture](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_mesh.ipynb)|\n\n| \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/densepose_render.png\" width=\"310\"/\u003e | \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/shapenet_render.png\" width=\"310\" height=\"310\"/\u003e\n|:------------------------------------------------------------:|:--------------------------------------------------:|\n| [Render DensePose data](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_densepose.ipynb)| [Load \u0026 Render ShapeNet data](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb)|\n\n| \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_textured_volume.gif\" width=\"310\"/\u003e | \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_nerf.gif\" width=\"310\" height=\"310\"/\u003e\n|:------------------------------------------------------------:|:--------------------------------------------------:|\n| [Fit Textured Volume](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_volume.ipynb)| [Fit A Simple Neural Radiance Field](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_simple_neural_radiance_field.ipynb)|\n\n| \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_textured_volume.gif\" width=\"310\"/\u003e | \u003cimg src=\"https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/implicitron_config.gif\" width=\"310\" height=\"310\"/\u003e\n|:------------------------------------------------------------:|:--------------------------------------------------:|\n| [Fit Textured Volume in Implicitron](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_volumes.ipynb)| [Implicitron Config System](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb)|\n\n\n\n\n\n## Documentation\n\nLearn more about the API by reading the PyTorch3D [documentation](https://pytorch3d.readthedocs.org/).\n\nWe also have deep dive notes on several API components:\n\n- [Heterogeneous Batching](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/batching.md)\n- [Mesh IO](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/meshes_io.md)\n- [Differentiable Rendering](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/renderer_getting_started.md)\n\n### Overview Video\n\nWe have created a short (~14 min) video tutorial providing an overview of the PyTorch3D codebase including several code examples. Click on the image below to watch the video on YouTube:\n\n\u003ca href=\"http://www.youtube.com/watch?v=Pph1r-x9nyY\"\u003e\u003cimg src=\"http://img.youtube.com/vi/Pph1r-x9nyY/0.jpg\" height=\"225\" \u003e\u003c/a\u003e\n\n## Development\n\nWe welcome new contributions to PyTorch3D and we will be actively maintaining this library! Please refer to [CONTRIBUTING.md](./.github/CONTRIBUTING.md) for full instructions on how to run the code, tests and linter, and submit your pull requests.\n\n## Development and Compatibility\n\n- `main` branch: actively developed, without any guarantee, Anything can be broken at any time\n  - REMARK: this includes nightly builds which are built from `main`\n  - HINT: the commit history can help locate regressions or changes\n- backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).\n\n## Contributors\n\nPyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.\n\nIn alphabetical order:\n\n* Amitav Baruah\n* Steve Branson\n* Krzysztof Chalupka\n* Jiali Duan\n* Luya Gao\n* Georgia Gkioxari\n* Taylor Gordon\n* Justin Johnson\n* Patrick Labatut\n* Christoph Lassner\n* Wan-Yen Lo\n* David Novotny\n* Nikhila Ravi\n* Jeremy Reizenstein\n* Dave Schnizlein\n* Roman Shapovalov\n* Olivia Wiles\n\n## Citation\n\nIf you find PyTorch3D useful in your research, please cite our tech report:\n\n```bibtex\n@article{ravi2020pytorch3d,\n    author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon\n                  and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},\n    title = {Accelerating 3D Deep Learning with PyTorch3D},\n    journal = {arXiv:2007.08501},\n    year = {2020},\n}\n```\n\nIf you are using the pulsar backend for sphere-rendering (the `PulsarPointRenderer` or `pytorch3d.renderer.points.pulsar.Renderer`), please cite the tech report:\n\n```bibtex\n@article{lassner2020pulsar,\n    author = {Christoph Lassner and Michael Zollh\\\"ofer},\n    title = {Pulsar: Efficient Sphere-based Neural Rendering},\n    journal = {arXiv:2004.07484},\n    year = {2020},\n}\n```\n\n## News\n\nPlease see below for a timeline of the codebase updates in reverse chronological order. We are sharing updates on the releases as well as research projects which are built with PyTorch3D. The changelogs for the releases are available under [`Releases`](https://github.com/facebookresearch/pytorch3d/releases),  and the builds can be installed using `conda` as per the instructions in [INSTALL.md](INSTALL.md).\n\n**[Oct 31st 2023]:**   PyTorch3D [v0.7.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.5) released.\n\n**[May 10th 2023]:**   PyTorch3D [v0.7.4](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.4) released.\n\n**[Apr 5th 2023]:**   PyTorch3D [v0.7.3](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.3) released.\n\n**[Dec 19th 2022]:**   PyTorch3D [v0.7.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.2) released.\n\n**[Oct 23rd 2022]:**   PyTorch3D [v0.7.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.1) released.\n\n**[Aug 10th 2022]:**   PyTorch3D [v0.7.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.0) released with Implicitron and MeshRasterizerOpenGL.\n\n**[Apr 28th 2022]:**   PyTorch3D [v0.6.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.2) released\n\n**[Dec 16th 2021]:**   PyTorch3D [v0.6.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.1) released\n\n**[Oct 6th 2021]:**   PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released\n\n**[Aug 5th 2021]:**   PyTorch3D [v0.5.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) released\n\n**[Feb 9th 2021]:** PyTorch3D [v0.4.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.4.0) released with support for implicit functions, volume rendering and a [reimplementation of NeRF](https://github.com/facebookresearch/pytorch3d/tree/main/projects/nerf).\n\n**[November 2nd 2020]:** PyTorch3D [v0.3.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.3.0) released, integrating the pulsar backend.\n\n**[Aug 28th 2020]:**   PyTorch3D [v0.2.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.5) released\n\n**[July 17th 2020]:**   PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501\n\n**[April 24th 2020]:**   PyTorch3D [v0.2.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.0) released\n\n**[March 25th 2020]:**   [SynSin](https://arxiv.org/abs/1912.08804) codebase released using PyTorch3D: https://github.com/facebookresearch/synsin\n\n**[March 8th 2020]:**   PyTorch3D [v0.1.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.1.1) bug fix release\n\n**[Jan 23rd 2020]:**   PyTorch3D [v0.1.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.1.0) released. [Mesh R-CNN](https://arxiv.org/abs/1906.02739) codebase released: https://github.com/facebookresearch/meshrcnn\n","funding_links":[],"categories":["Python","Tools and Libraries","Computer Vision","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Pytorch \u0026 related libraries","CV","其他_机器视觉","计算机视觉 (CV)","图像数据与CV","3D Vision \u0026 Point Clouds"],"sub_categories":["Self-Supervised Learning","Others","CV｜计算机视觉:","CV:","网络服务_其他"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Fpytorch3d","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffacebookresearch%2Fpytorch3d","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Fpytorch3d/lists"}