Point Clouds Downsampling Based on Complementary Attention and Contrastive Learning

This paper presents a novel method for point clouds down-sampling. We formulate the sampling task as an optimal permutation problem and develop two techniques, the com-plementary attention module and contrastive learning mech-anism, to enable it. The complementary attention module assigns large weights to point-wise features to emphasize fea-tures related to selected and discarded points. We optimize the network by introducing the contrastive learning mecha-nism, which minimizes feature discrepancy of the discarded points while maximizing feature separation between the se-lected points. It is evaluated on ModelNet40 and ShapeNet-Core datasets for classification and reconstruction tasks and achieves promising results.