PyTorch3D is a library of reusable components for 3D computer vision research, developed by Facebook AI Research. It provides efficient tools for manipulating triangle meshes, differentiable rendering, and integrating 3D data with deep learning models, targeting researchers and engineers working with 3D data.

How It Works

PyTorch3D offers a PyTorch-native tensor-based data structure for meshes and efficient operators that support minibatches of heterogeneous data and GPU acceleration. Its core advantage lies in its fully differentiable pipeline, enabling end-to-end training of deep learning models that incorporate 3D geometry and rendering.

Quick Start & Requirements

• Installation instructions are detailed in INSTALL.md.
• Requires PyTorch. GPU acceleration is recommended.
• Tutorials and documentation are available: Tutorials, Documentation.

Highlighted Details

• Efficient operations on triangle meshes, including projective transformations and graph convolutions.
• Differentiable mesh renderer and support for implicit representations (Implicitron).
• Handles minibatches of heterogeneous 3D data.
• Used in research projects like Mesh R-CNN.

Maintenance & Community

Maintained by the Facebook AI Research Computer Vision Team. Active development with regular releases. Contributions are welcomed via pull requests.

Licensing & Compatibility

Released under the BSD License, which is permissive for commercial use and closed-source linking.

Limitations & Caveats

The main branch is under active development and may contain breaking changes without notice. Backward compatibility between releases is not guaranteed, though efforts are made to communicate significant changes.