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.
@article{arxiv.2207.01174,
title = {Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding},
author = {Haoyi Xiu and Xin Liu and Weimin Wang and Kyoung-Sook Kim and Takayuki Shinohara and Qiong Chang and Masashi Matsuoka},
journal= {arXiv preprint arXiv:2207.01174},
year = {2022}
}