PyTorch3D provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).
环境以及版本依赖：
Key features include:
* Python 3.8,3.9 or 3.10
- Data structure for storing and manipulating triangle meshes
- dtk-23.10
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- pytorch-2.1
- A differentiable mesh renderer
- 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/))
PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
## 安装
For this reason, all operators in PyTorch3D:
- Are implemented using PyTorch tensors
### 环境准备
- 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).
See 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.
| [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) |
| [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)|
We 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:
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.
## Development and Compatibility
-`main` branch: actively developed, without any guarantee, Anything can be broken at any time
- REMARK: this includes nightly builds which are built from `main`
- HINT: the commit history can help locate regressions or changes
- backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).
## Contributors
PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.
In alphabetical order:
* Amitav Baruah
* Steve Branson
* Krzysztof Chalupka
* Jiali Duan
* Luya Gao
* Georgia Gkioxari
* Taylor Gordon
* Justin Johnson
* Patrick Labatut
* Christoph Lassner
* Wan-Yen Lo
* David Novotny
* Nikhila Ravi
* Jeremy Reizenstein
* Dave Schnizlein
* Roman Shapovalov
* Olivia Wiles
## Citation
If you find PyTorch3D useful in your research, please cite our tech report:
```bibtex
@article{ravi2020pytorch3d,
author={Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
title={Accelerating 3D Deep Learning with PyTorch3D},
journal={arXiv:2007.08501},
year={2020},
}
```
```
If you are using the pulsar backend for sphere-rendering (the `PulsarPointRenderer` or `pytorch3d.renderer.points.pulsar.Renderer`), please cite the tech report:
```bibtex
@article{lassner2020pulsar,
author={Christoph Lassner and Michael Zollh\"ofer},
Please 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).
**[Aug 10th 2022]:** PyTorch3D [v0.7.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.0) released with Implicitron and MeshRasterizerOpenGL.
**[Apr 28th 2022]:** PyTorch3D [v0.6.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.2) released
### 编译安装
**[Dec 16th 2021]:** PyTorch3D [v0.6.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.1) released
* 拉取代码
**[Oct 6th 2021]:** PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released
**[Aug 5th 2021]:** PyTorch3D [v0.5.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) released
* 若使用 `GPU` 支持，需要设置环境变量`FORCE_CUDA`为1
**[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).
```shell
export FORCE_CUDA=1
```
**[November 2nd 2020]:** PyTorch3D [v0.3.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.3.0) released, integrating the pulsar backend.
- 执行编译命令
**[Aug 28th 2020]:** PyTorch3D [v0.2.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.5) released
```shell
cd path/to/pytorch3d
pip install-e .
```
**[July 17th 2020]:** PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501
* 当编译出现错误时，参考以下说明：
**[April 24th 2020]:** PyTorch3D [v0.2.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.0) released
* 报错提示缺少 `intel-mkl` 相关库，应安装 `intel-mkl`
**[March 25th 2020]:**[SynSin](https://arxiv.org/abs/1912.08804) codebase released using PyTorch3D: https://github.com/facebookresearch/synsin
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
- 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/))
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).
See 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.
## Installation
For detailed instructions refer to [INSTALL.md](INSTALL.md).
## License
PyTorch3D is released under the [BSD 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/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/bundle_adjustment.ipynb) |
| [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)|
We 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:
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.
## Development and Compatibility
-`main` branch: actively developed, without any guarantee, Anything can be broken at any time
- REMARK: this includes nightly builds which are built from `main`
- HINT: the commit history can help locate regressions or changes
- backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).
## Contributors
PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.
In alphabetical order:
* Amitav Baruah
* Steve Branson
* Krzysztof Chalupka
* Jiali Duan
* Luya Gao
* Georgia Gkioxari
* Taylor Gordon
* Justin Johnson
* Patrick Labatut
* Christoph Lassner
* Wan-Yen Lo
* David Novotny
* Nikhila Ravi
* Jeremy Reizenstein
* Dave Schnizlein
* Roman Shapovalov
* Olivia Wiles
## Citation
If you find PyTorch3D useful in your research, please cite our tech report:
```bibtex
@article{ravi2020pytorch3d,
author={Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
title={Accelerating 3D Deep Learning with PyTorch3D},
journal={arXiv:2007.08501},
year={2020},
}
```
If you are using the pulsar backend for sphere-rendering (the `PulsarPointRenderer` or `pytorch3d.renderer.points.pulsar.Renderer`), please cite the tech report:
```bibtex
@article{lassner2020pulsar,
author={Christoph Lassner and Michael Zollh\"ofer},
Please 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).
**[Aug 10th 2022]:** PyTorch3D [v0.7.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.0) released with Implicitron and MeshRasterizerOpenGL.
**[Apr 28th 2022]:** PyTorch3D [v0.6.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.2) released
**[Dec 16th 2021]:** PyTorch3D [v0.6.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.1) released
**[Oct 6th 2021]:** PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released
**[Aug 5th 2021]:** PyTorch3D [v0.5.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) released
**[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).
**[November 2nd 2020]:** PyTorch3D [v0.3.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.3.0) released, integrating the pulsar backend.
**[Aug 28th 2020]:** PyTorch3D [v0.2.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.5) released
**[July 17th 2020]:** PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501
**[April 24th 2020]:** PyTorch3D [v0.2.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.0) released
**[March 25th 2020]:**[SynSin](https://arxiv.org/abs/1912.08804) codebase released using PyTorch3D: https://github.com/facebookresearch/synsin
