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arXiv · 2207.01174

Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding

Haoyi Xiu·Xin Liu·Weimin Wang·Kyoung-Sook Kim·Takayuki Shinohara·Qiong Chang·Masashi Matsuoka
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Abstract

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.

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Haoyi Xiu, Xin Liu, Weimin Wang, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka. 2022-07-04. Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding. https://arxiv.org/abs/2207.01174

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