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

•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.