This tutorial uses a combination of slides and interactive jupyter notebooks using the
se3cnn framework to present fundamental concepts about Euclidean equivariant neural networks: data types, equivariant operations, how symmetry effects simple tasks, and how to construct the building blocks of
se3cnn. This tutorial was originally presented at IPAM at UCLA on November 14, 2019.
Convolutional neural networks for point clouds and voxels with Euclidean symmetries (3D translation and 3D rotation). The current code (on the
point branch) is a merger of the work from Tensor field networks and 3D Steerable CNNs.