Implicit Surface Contrastive Clustering for LiDAR Point Clouds

Self-supervised pretraining on large unlabeled datasets has shown tremendous success in improving the task performance of many 2D and small scale 3D computer vision tasks. However, the popular pretraining approaches have not been impactfully applied to outdoor LiDAR point cloud perception due to the latter's scene complexity and wide range. We propose a new self-supervised pretraining method ISCC with two novel pretext tasks for LiDAR point clouds. The first task uncovers semantic information by sorting local groups of points in the scene into a globally consistent set of semantically meaningful clusters using contrastive learning, complemented by a second task which reasons about precise surfaces of various parts of the scene through implicit surface reconstruction to learn geometric structures. We demonstrate their effectiveness through transfer learning on 3D object detection and semantic segmentation in real world LiDAR scenes. We further design an unsupervised semantic grouping task to show that our approach learns highly semantically meaningful features.