  Learning point clouds is challenging due to the lack of connectivity
information, i.e., edges. Although existing edge-aware methods can improve the
performance by modeling edges, how edges contribute to the improvement is
unclear. In this study, we propose a method that automatically learns to
enhance/suppress edges while keeping the its working mechanism clear. First, we
theoretically figure out how edge enhancement/suppression works. Second, we
experimentally verify the edge enhancement/suppression behavior. Third, we
empirically show that this behavior improves performance. In general, we
observe that the proposed method achieves competitive performance in point
cloud classification and segmentation tasks.
