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.

Don't forget to tag @facebookresearch in your comment, otherwise they may not be notified.

Authors community post
Share this project
Similar projects
3D Photography using Context-aware Layered Depth Inpainting
A multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.
Shape and Viewpoint without Keypoints
Recover the 3D shape, pose and texture from a single image, trained on an image collection without any ground truth 3D shape, multi-view, camera ...
PIFuHD: High-Resolution 3D Human Digitization
This repository contains a pytorch implementation of "Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization".
MediaPipe
Simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.
Top collections