Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

@article{Luo2022EquivariantPC,
  title={Equivariant Point Cloud Analysis via Learning Orientations for Message Passing},
  author={Shitong Luo and Jiahan Li and Jiaqi Guan and Yufeng Su and Chaoran Cheng and Jian Peng and Jianzhu Ma},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.14486}
}
Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, sim-plicity, and expressiveness — some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacrifice flexible transformations. In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme. We find… 

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