A novel GCN-based point cloud classification model robust to pose variances

作者：

Highlights：

• Different from the point cloud representation of the Cartesian coordinate system, a novel rotation-independent auxiliary network is proposed with the aid of the spherical coordinate system.

• In order to cope with the challenge of feature extraction caused by the disorder of point cloud data itself, a novel graph convolution network was proposed.

• In view of the particularity of point cloud data, how to effectively extract its global and local features and how to deal with the training problem of point cloud unbalanced data is also considered in this study.

摘要

•Different from the point cloud representation of the Cartesian coordinate system, a novel rotation-independent auxiliary network is proposed with the aid of the spherical coordinate system.•In order to cope with the challenge of feature extraction caused by the disorder of point cloud data itself, a novel graph convolution network was proposed.•In view of the particularity of point cloud data, how to effectively extract its global and local features and how to deal with the training problem of point cloud unbalanced data is also considered in this study.

论文关键词：Point cloud,Pose robust,Graph convolutional network,Classification

论文评审过程：Received 22 September 2020, Revised 3 August 2021, Accepted 11 August 2021, Available online 13 August 2021, Version of Record 19 August 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108251