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Lint codebase

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Summary: Lint codebase

Reviewed By: bottler

Differential Revision: D29263057

fbshipit-source-id: ac97f01d2a79fead3b09c2cbb21b50ce688a577d
This commit is contained in:
Patrick Labatut
2021-06-22 03:44:27 -07:00
committed by Facebook GitHub Bot
parent ce60d4b00e
commit 7e43f29d52
7 changed files with 7 additions and 10 deletions
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docs/README.md
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## Setup
### Install dependencies
@@ -12,7 +11,7 @@ pip install -U recommonmark mock sphinx sphinx_rtd_theme sphinx_markdown_tables
We want to include the root readme as an overview. Before generating the docs create a symlink to the root readme.
```
cd docs
cd docs
ln -s ../README.md overview.md
```

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docs/notes/why_pytorch3d.md
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# Why PyTorch3D
Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as [Mesh R-CNN](https://github.com/facebookresearch/meshrcnn) and [C3DPO](https://github.com/facebookresearch/c3dpo_nrsfm), we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.
Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as [Mesh R-CNN](https://github.com/facebookresearch/meshrcnn) and [C3DPO](https://github.com/facebookresearch/c3dpo_nrsfm), we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.
In PyTorch3D we have included efficient 3D operators, heterogeneous batching capabilities, and a modular differentiable rendering API, to equip researchers in this field with a much needed toolkit to implement cutting-edge research with complex 3D inputs.

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docs/tutorials/data/cow_mesh/README.md
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# Acknowledgements
Thank you to Keenen Crane for allowing the cow mesh model to be used freely in the public domain.