Self-supervised latent feature learning for partial point clouds recognition

Abstract

3D vision perception, especially point clouds classification is fundamental and popular in safety-critical systems such as autonomous driving and robotics automation control. However, their robustness against incomplete partial point clouds in practical scenes is less studied, limiting real-world deployment. In this paper, we propose to improve the robustness and generalization of 3D models on partial point clouds by self-supervised latent feature learning. Unlike those data augmentation methods that generate partial point clouds by geometric transforms in coordinate space (e.g drop local structure or remove global points), we regard the partial data as a transformation in latent feature space. We explicitly learn the perspective transformation of partial point clouds and implicitly learn the occlusion transformation in the latent feature space by self-supervised learning. Different from previous methods that are validated on generated data, we test our method on point clouds completion datasets (e.g. PCN and MVP) which contain both complete and partial point clouds and have been widely used. Extensive experiments show that our proposed method consistently improves the robustness of state-of-the-art methods on the partial point clouds dataset.