Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis
This addresses a specific issue in 3D point cloud analysis for applications such as computer vision and robotics, but it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of suboptimal graph structures in graph-based methods for 3D point cloud analysis, which suffer from sparse connections at boundaries and noisy connections in junctions, by proposing a method that integrates graph smoothing with enhanced local geometry learning, and experimental results on real-world datasets validate its effectiveness in tasks like classification and segmentation.
Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph structures, particularly due to sparse connections at boundary points and noisy connections in junction areas. To address these challenges, we propose a novel method that integrates a graph smoothing module with an enhanced local geometry learning module. Specifically, we identify the limitations of conventional graph structures, particularly in handling boundary points and junction areas. In response, we introduce a graph smoothing module designed to optimize the graph structure and minimize the negative impact of unreliable sparse and noisy connections. Based on the optimized graph structure, we improve the feature extract function with local geometry information. These include shape features derived from adaptive geometric descriptors based on eigenvectors and distribution features obtained through cylindrical coordinate transformation. Experimental results on real-world datasets validate the effectiveness of our method in various point cloud learning tasks, i.e., classification, part segmentation, and semantic segmentation.