  Point clouds have attracted increasing attention. Significant progress has
been made in methods for point cloud analysis, which often requires costly
human annotation as supervision. To address this issue, we propose a novel
self-contrastive learning for self-supervised point cloud representation
learning, aiming to capture both local geometric patterns and nonlocal semantic
primitives based on the nonlocal self-similarity of point clouds. The
contributions are two-fold: on the one hand, instead of contrasting among
different point clouds as commonly employed in contrastive learning, we exploit
self-similar point cloud patches within a single point cloud as positive
samples and otherwise negative ones to facilitate the task of contrastive
learning. On the other hand, we actively learn hard negative samples that are
close to positive samples for discriminative feature learning. Experimental
results show that the proposed method achieves state-of-the-art performance on
widely used benchmark datasets for self-supervised point cloud segmentation and
transfer learning for classification.
