• Corpus ID: 222140724

On the Universality of Rotation Equivariant Point Cloud Networks

@article{Dym2020OnTU,
  title={On the Universality of Rotation Equivariant Point Cloud Networks},
  author={Nadav Dym and Haggai Maron},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.02449}
}
Learning functions on point clouds has applications in many fields, including computer vision, computer graphics, physics, and chemistry. Recently, there has been a growing interest in neural architectures that are invariant or equivariant to all three shape-preserving transformations of point clouds: translation, rotation, and permutation. In this paper, we present a first study of the approximation power of these architectures. We first derive two sufficient conditions for an equivariant… 

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