PointCluster: Deep Clustering of 3-D Point Clouds With Semantic Pseudo-Labeling

Point cloud classification is a fundamental problem in 3-D point cloud analysis. However, most existing methods are supervised, which requires costly and laborious annotations of large-scale point cloud datasets. This severely limits the practical applicability of point clouds. Therefore, exploring point cloud clustering methods, which can group point clouds into semantically meaningful clusters in an unsupervised manner, is of great importance. However, this remains a formidable challenge for humans. Here, we present PointCluster, a novel framework for deep clustering of 3-D point clouds. To enable accurate and reliable self-supervision for the clustering process, the framework introduces two semantic pseudo-labeling algorithms: prototype pseudo-labeling and reliable pseudo-labeling. We devise a three-step training process for the clustering network. First, we adopt a cross-modal representation learning approach to optimize the feature model. Second, we freeze the network parameters of the feature model and apply the prototype pseudo-labeling algorithm to optimize the clustering heads separately. Third, we use the reliable pseudo-labeling algorithm to jointly train the feature model and the clustering head in a semi-supervised manner, which enhances the overall clustering performance. The experimental results demonstrate that PointCluster achieves the state-of-the-art clustering results on public datasets such as ShapeNet. Moreover, our method narrows the gap between unsupervised point cloud clustering and supervised point cloud classification, offering a new perspective for the point cloud classification task.