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

TL;DR

This paper introduces a method for learning point clouds that automatically enhances or suppresses edges, improving classification and segmentation performance while providing theoretical insights into the role of edges.

Contribution

The study presents a novel approach that explicitly models edge enhancement and suppression, with theoretical analysis and empirical validation of its effectiveness.

Findings

The method achieves competitive results in point cloud classification.

The approach improves segmentation accuracy.

Theoretical analysis clarifies how edge manipulation affects learning.

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.