PyTorch3D provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).
* 用于存储和操作三角网格的数据结构
* 高效的三角网格操作（投影变换、图卷积、采样、损失函数等）
* 可微分网格渲染器
Key features include:
PyTorch3D 专为与深度学习方法无缝集成而设计，可用于预测和处理 3D 数据。因此，PyTorch3D 的所有算子均具备以下特性：
- Data structure for storing and manipulating triangle meshes
* 基于 PyTorch 张量实现
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
* 支持异构数据的批量处理
- A differentiable mesh renderer
* 可微分计算
- 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/))
* 支持 GPU 加速
PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
## 2 安装
For this reason, all operators in PyTorch3D:
- Are implemented using PyTorch tensors
组件支持组合
- 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
### 2.2 使用源码编译方式安装
If you find PyTorch3D useful in your research, please cite our tech report:
#### 2.2.1 编译环境准备
```bibtex
提供基于 fastpt 不转码编译：
@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:
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.
**[Oct 6th 2021]:** PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released
## 3 验证
- 执行下面的命令获取版本号：
```bash
source /usr/local/bin/fastpt -E
**[Aug 5th 2021]:** PyTorch3D [v0.5.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) released
python
>>> import pytorch3d
>>> pytorch3d.__version__
'0.7.8'
```
**[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).
- 执行下面的命令测试组件：
**[November 2nd 2020]:** PyTorch3D [v0.3.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.3.0) released, integrating the pulsar backend.
```bash
pip install omegaconf
pip install tabulate
pip install pyopengl
pip install pycuda
pip install visdom
pip install lpips
pip install plotly
**[Aug 28th 2020]:** PyTorch3D [v0.2.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.5) released
source /usr/local/bin/fastpt -E
**[July 17th 2020]:** PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501
