PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.
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
PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3D:
Are implemented using PyTorch tensors
Can handle minibatches of hetereogenous data
Can be differentiated
Can utilize GPUs for acceleration
Within FAIR, PyTorch3D has been used to power research projects such as Mesh R-CNN.