mlsanityData to 14 Oct 2026

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

arXiv 2207.01174 ↗First posted 4 Jul 2022cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 58 references that are in the map. First posted on arXiv 4 Jul 2022.

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37 of 58 references.

References · 37

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25 more
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