3D-Rotation-Equivariant Quaternion Neural Networks

Wen Shen; Binbin Zhang; Shikun Huang; Zhihua Wei; Quanshi Zhang

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arXiv · 1911.09040

3D-Rotation-Equivariant Quaternion Neural Networks

Wen Shen·Binbin Zhang·Shikun Huang·Zhihua Wei·Quanshi Zhang

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Abstract

This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs). We find that when a neural network uses quaternion features under certain conditions, the network feature naturally has the rotation-equivariance property. Rotation equivariance means that applying a specific rotation transformation to the input point cloud is equivalent to applying the same rotation transformation to all intermediate-layer quaternion features. Besides, the REQNN also ensures that the intermediate-layer features are invariant to the permutation of input points. Compared with the original neural network, the REQNN exhibits higher rotation robustness.

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BibTeX RIS

Wen Shen, Binbin Zhang, Shikun Huang, Zhihua Wei, Quanshi Zhang. 2019-11-20. 3D-Rotation-Equivariant Quaternion Neural Networks. https://arxiv.org/abs/1911.09040

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