Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and Aggregation

We propose a co-part segmentation method that takes a set of point clouds of the same category as input where neither a ground truth label nor a prior network is required. With difficulties caused by the label absence, we formulate the co-part segmentation task into two subtasks, including superpoint generation and part aggregation. In the first subtask, our superpoint generation network divides each point cloud into homogeneous partitions, each called superpoint, while in the second subtask, these superpoints are further aggregated into a few semantic parts via our part aggregation network. We introduce the coupled attention blocks in the part aggregation network to explicitly enforce semantic consistency in the segmentation by exploiting intra-, inter-, and paired-cloud geometrical information by minimizing the devised intra-, inter-, and paired-cloud losses, respectively. The intra-cloud loss triggers a semantic segmentation in each point cloud, while the inter-cloud loss considers all clouds to enforce their semantic consistency. The paired-cloud loss is designed to ensure that each part of one point cloud can be discriminatively reconstructed from the superpoints of another point cloud. We perform experiments on two benchmark datasets, ShapeNet part and COSEG, and provide quantitative and qualitative results to demonstrate the superiority of our method over existing methods. We also show that the proposed method can help several downstream tasks, including semi-supervised part segmentation and data augmentation for shape classification. The code for this work will be publicly available upon the paper's publication.