Get Started Install PyTorch3D (following the instructions here) Try a few 3D operators e.g. compute the chamfer loss between two meshes:

The PyTorch3D backward pass returns gradients for zbuf, dist and bary_coords. Returning intermediate variables from rasterization has an associated memory cost.

Welcome to the PyTorch3D Tutorials Here you can learn about the structure and applications of PyTorch3D from examples which are in the form of ipython notebooks.

The following figure outlines the conventions used PyTorch3D. For example, given a teapot mesh, the world coordinate frame, camera coordinate frame and image are shown in the figure below.

In this tutorial we learnt how to load a textured mesh from an obj file, initialize a PyTorch3D datastructure called Meshes, set up an Renderer consisting of a Rasterizer and a Shader, and …

Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as Mesh R …

However, if users wish to use cameras with the PyTorch3D renderer, they need to abide to PyTorch3D's coordinate system assumptions (read below). We provide instantiations of …

In this tutorial we learnt how to load a mesh from an obj file, initialize a PyTorch3D datastructure called Meshes, set up an optimization loop and use four different PyTorch3D mesh loss …

A renderer in PyTorch3D is composed of a rasterizer and a shader which each have a number of subcomponents such as a camera (orthographic/perspective). Here we initialize some of these …

# BatchingBatching In deep learning, every optimization step operates on multiple input examples for robust training. Thus, efficient batching is crucial. For image inputs, batching is …