通过图平滑提升点云边界与交界处的几何学习效果
Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis
- 引入图平滑模块优化稀疏和噪声连接的图结构
- 在真实数据集上实现分类、分割任务的性能提升
- 适合点云几何建模与三维视觉任务的研究者
基于图的方法在捕捉点云中点之间的关系方面表现良好，但常因边界点连接稀疏和交汇区域连接噪声而表现不佳。为此，我们提出一种新方法，将图平滑模块与增强的局部几何学习模块结合。首先识别传统图结构在处理边界点和交汇区域时的局限性；随后设计图平滑模块以优化图结构，减少不可靠连接的影响。基于优化后的图结构，我们改进特征提取函数，引入基于特征向量的自适应几何描述符所生成的形状特征，以及通过柱坐标变换获得的分布特征。在真实世界数据集上的实验表明，该方法在点云分类、部件分割和语义分割等任务中均有效提升性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。