pytorch3d

PyTorch3D is a library from Facebook AI Research (FAIR) designed to provide efficient, reusable components for 3D computer vision research using PyTorch. It focuses on deep learning with 3D data, offering a range of tools that integrate smoothly with PyTorch's tensor-based workflows. Key features include: - A data structure for storing and manipulating triangle meshes. - Efficient operations on meshes, such as projective transformations, graph convolution, sampling, and loss functions. - A differentiable mesh renderer, enabling gradient-based optimization of 3D scenes. - Implicitron, a modular framework for new-view synthesis via implicit representations, including a reimplementation of NeRF (Neural Radiance Fields). All operators in PyTorch3D are implemented using PyTorch tensors, support minibatches of heterogeneous data, are differentiable, and can leverage GPU acceleration. This makes it suitable for research projects like Mesh R-CNN and SynSin. The library includes tutorials covering topics like deforming meshes, bundle adjustment, rendering textured meshes and point clouds, camera optimization, and fitting neural radiance fields. It also provides comprehensive documentation and deep-dive notes on batching, mesh I/O, and differentiable rendering. PyTorch3D is released under the BSD License and is actively maintained by the FAIR Computer Vision Team. It has been used in various research projects and is available via conda for installation.