3D-Rotation-Equivariant Quaternion Neural Networks
Published in ECCV, 2020
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, 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.
Recommended citation: Shen W., Zhang B., Huang S., Wei Z., Zhang Q.: 3D-Rotation-Equivariant Quaternion Neural Networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 531-547 (2020)