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Update README with links to blog posts (#43)

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Summary: Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/43

Reviewed By: bottler

Differential Revision: D42791756

Pulled By: nikhilaravi

fbshipit-source-id: 498399c1ce30bb095579c4d66b6314a6aa846df3
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Nikhila Ravi 2023-01-27 01:57:54 -08:00 committed by Facebook GitHub Bot
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@ -12,7 +12,7 @@ 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.
- 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:
@ -24,6 +24,8 @@ For this reason, all operators in PyTorch3D:
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).