PointGLR: Unsupervised Structural Representation Learning of 3D Point Clouds.

Abstract

This work explores the use of global and local structures of 3D point clouds as a free and powerful supervision signal for representation learning. Local and global patterns of a 3D object are closely related. Although each part of an object is incomplete, the underlying attributes about the object are shared among all parts, which makes reasoning about the whole object from a single part possible. We hypothesize that a powerful representation of a 3D object should model the attributes that are shared between parts and the whole object, and distinguishable from other objects. Based on this hypothesis, we propose a new framework to learn point cloud representations by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape. Moreover, we extend the unsupervised structural representation learning method to more complex 3D scenes. By introducing structural proxies as the intermediate-level representations between local and global ones, we propose a hierarchical reasoning scheme among local parts, structural proxies, and the overall point cloud to learn powerful 3D representations in an unsupervised manner. Extensive experimental results demonstrate that the unsupervised representations can be very competitive alternatives of supervised representations in discriminative power, and exhibit better performance in generalization ability and robustness. Our method establishes the new state-of-the-art of unsupervised/few-shot 3D object classification and part segmentation. We also show our method can serve as a simple yet effective regime for model pre-training on 3D scene segmentation and detection tasks. We expect our observations to offer a new perspective on learning better representations from data structures instead of human annotations for point cloud understanding.