Group-equivariant convolutional neural networks for 3D point clouds

A system including one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to implement a group-equivariant convolutional neural network configured to process a network input including a set of 3D points to generate an output tensor representing transformed features of the network input is described. The group-equivariant convolutional neural network includes a grouping layer, a switching layer, a pre-processing layer, a group-equivariant layer, a first subnetwork, an average pooling layer, a second subnetwork, an output subnetwork, and a final output layer.