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https://github.com/facebookresearch/pytorch3d.git
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78 lines
3.9 KiB
Markdown
78 lines
3.9 KiB
Markdown
<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/pytorch3dlogo.png" width="900"/>
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[](https://circleci.com/gh/facebookresearch/pytorch3d)
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[](https://anaconda.org/pytorch3d/pytorch3d)
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# Introduction
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PyTorch3d provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).
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Key features include:
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- Data structure for storing and manipulating triangle meshes
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- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
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- A differentiable mesh renderer
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PyTorch3d is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
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For this reason, all operators in PyTorch3d:
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- Are implemented using PyTorch tensors
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- Can handle minibatches of hetereogenous data
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- Can be differentiated
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- Can utilize GPUs for acceleration
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Within FAIR, PyTorch3d has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739).
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## Installation
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For detailed instructions refer to [INSTALL.md](INSTALL.md).
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## License
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PyTorch3d is released under the [BSD-3-Clause License](LICENSE).
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## Tutorials
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Get started with PyTorch3d by trying one of the tutorial notebooks.
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|<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/dolphin_deform.gif" width="310"/>|<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/bundle_adjust.gif" width="310"/>|
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|:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------:|
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| [Deform a sphere mesh to dolphin](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/bundle_adjustment.ipynb) |
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| <img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/render_textured_mesh.gif" width="310"/> | <img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/camera_position_teapot.gif" width="310" height="310"/>
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|:------------------------------------------------------------:|:--------------------------------------------------:|
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| [Render textured meshes](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/render_textured_meshes.ipynb)| [Camera position optimization](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb)|
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## Documentation
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Learn more about the API by reading the PyTorch3d [documentation](https://pytorch3d.readthedocs.org/).
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We also have deep dive notes on several API components:
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- [Heterogeneous Batching](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/batching.md)
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- [Mesh IO](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/meshes_io.md)
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- [Differentiable Rendering](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/renderer_getting_started.md)
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## Development
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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.
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## Contributors
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PyTorch3d is written and maintained by the Facebook AI Research Computer Vision Team.
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## Citation
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If you find PyTorch3d useful in your research, please cite:
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```bibtex
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@misc{ravi2020pytorch3d,
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author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
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and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
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title = {PyTorch3D},
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howpublished = {\url{https://github.com/facebookresearch/pytorch3d}},
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year = {2020}
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}
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```
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