A General Framework for SO(3)-Equivariant Networks
Vector Neurons
We introduce a general framework built on top of what we call Vector Neurons for creating SO(3) equivariant neural networks. Extending neurons from single scalars to 3D vectors, our vector neurons transport SO(3) actions to latent spaces and provide a framework for building equivariance in common neural operations including linear layers, non-linearities, pooling, and normalization.
Created by Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas Guibas.
Overview
vnn is the author’s implementation of Vector Neuron Networks with PointNet and DGCNN backbones. The current version only supports input data without normals.
Data Preparation
- Classification: Download ModelNet40 and save in
data/modelnet40_normal_resampled/. - Part Segmentation: Download ShapeNet and save in
data/shapenetcore_partanno_segmentation_benchmark_v0_normal/.
Usage
Classification on ModelNet40