CCPoint: Contrasting Corrupted Point Clouds for Self-Supervised Representation Learning

Self-supervised Learning (SSL), including mainstream contrastive learning, has achieved significant success in learning visual representations without the need for data annotations in 3D vision. While most contrastive learning methods focus on instance-level information through random affine transformations, they pay limited attention to the intrinsic structures within point clouds. In this work, we propose a novel SSL paradigm for point cloud representation learning, called CCPoint, which incorporates a novel form of data corruption as a negative augmentation strategy. Specifically, we degrade the input point cloud with various corruptions and conduct contrastive learning among the augmented, raw, and corrupted points to learn robust and discriminative representations. To preserve the semantic structure of the point cloud even under heavy degradation, an auxiliary reconstruction decoder is introduced into the corruption branch to provide an additional supervision signal. We explore four families of corruptions—affine, noise, masking, and combined transformations. Different from previous methods that rely on multi-modal data or complex network architectures, CCPoint achieves state-of-the-art performance on three widely used datasets (ModelNet40, ScanObjectNN, and ShapeNetPart) with a lightweight and efficient structure, reaching top linear accuracies of 92.4% and 86.2% on ModelNet40 and ScanObjectNN, respectively.