Enhanced Local and Global Learning for Rotation-Invariant Point Cloud Representation

Abstract

Various studies have been undertaken to learn point cloud representations that are both discriminative and robust. However, most of them suffer from rotation disturbance and insufficient labeled data. To solve the problem of rotation disturbance, we propose a novel rotation-invariant network called ELGANet that is equipped with the following two core modules: enhanced local representation learning module and global alignment module. The enhanced local representation learning module captures the geometric relationship among the neighbors defined in both 3-D Cartesian space and a latent space to exploit the local context and long-distance context. The global alignment module is devised to address the lack of global information and supplement the absolute locations of points by adaptively generating the rotation-invariant coordinates. For the issue of label dependence, we further propose an unsupervised learning network ELGANet-U that can still generate a discriminative and rotation-invariant representation without human supervision. Extensive experiments on both ModelNet and ScanObjectNN have demonstrated that our ELGANet is superior to other state-of-the-art methods on the premise of ensuring rotation invariance. Furthermore, the representation generated by our ELGANet-U also achieves a comparable performance to that of supervised learning.