facebookresearch/pytorch3d

What it solves

PyTorch3D is a library designed to simplify 3D Computer Vision research by providing efficient, reusable components for predicting and manipulating 3D data using PyTorch. It bridges the gap between 3D data structures and deep learning frameworks, allowing researchers to integrate 3D operations into neural networks.

How it works

The library implements all operators using PyTorch tensors, ensuring they are differentiable and can utilize GPU acceleration. It provides specialized data structures for triangle meshes and a differentiable mesh renderer. It also includes Implicitron, a framework for new-view synthesis via implicit representations.

Who it’s for

It is primarily aimed at researchers in 3D Computer Vision and deep learning who need to perform operations on 3D meshes, point clouds, and volumes, or who are building models for 3D reconstruction and synthesis.

Highlights

• Differentiable Rendering: Includes a mesh renderer that allows gradients to flow back through the rendering process.
• Mesh Manipulation: Efficient operations for projective transformations, graph convolution, and sampling on triangle meshes.
• GPU Acceleration: All operators are optimized for PyTorch tensors and support minibatches of heterogeneous data.
• Implicit Representations: Support for implicit functions, volume rendering, and a framework for new-view synthesis (Implicitron).