[](https://circleci.com/gh/facebookresearch/pytorch3d) [](https://anaconda.org/pytorch3d/pytorch3d) # Introduction PyTorch3D provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org). Key features include: - Data structure for storing and manipulating triangle meshes - Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions) - A differentiable mesh renderer PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3D: - Are implemented using PyTorch tensors - Can handle minibatches of hetereogenous data - Can be differentiated - Can utilize GPUs for acceleration Within FAIR, PyTorch3D has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739). ## Installation For detailed instructions refer to [INSTALL.md](INSTALL.md). ## License PyTorch3D is released under the [BSD-3-Clause License](LICENSE). ## Tutorials Get started with PyTorch3D by trying one of the tutorial notebooks. ||| |:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------:| | [Deform a sphere mesh to dolphin](https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/bundle_adjustment.ipynb) | | | |:------------------------------------------------------------:|:--------------------------------------------------:| | [Render textured meshes](https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/render_textured_meshes.ipynb)| [Camera position optimization](https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb)| ## Documentation Learn more about the API by reading the PyTorch3D [documentation](https://pytorch3d.readthedocs.org/). We also have deep dive notes on several API components: - [Heterogeneous Batching](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/batching.md) - [Mesh IO](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/meshes_io.md) - [Differentiable Rendering](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/renderer_getting_started.md) ## Development We 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. ## Contributors PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team. ## Citation If you find PyTorch3D useful in your research, please cite: ```bibtex @misc{ravi2020pytorch3d, author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari}, title = {PyTorch3D}, howpublished = {\url{https://github.com/facebookresearch/pytorch3d}}, year = {2020} } ```