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.

翻译：基于图的方法已被证明在三维点云分析中能有效捕捉点之间的关系。然而，这些方法常因图结构欠佳而受限，尤其是边界点处的稀疏连接与交汇区域的噪声连接。为应对这些挑战，我们提出了一种新颖方法，将图平滑模块与增强的局部几何学习模块相结合。具体而言，我们指出了传统图结构在处理边界点与交汇区域时的局限性。为此，我们引入了一个图平滑模块，旨在优化图结构并最小化不可靠稀疏连接与噪声连接的负面影响。基于优化后的图结构，我们利用局部几何信息改进了特征提取函数，包括基于特征向量的自适应几何描述符导出的形状特征，以及通过柱坐标变换获得的分布特征。在真实数据集上的实验结果表明，我们的方法在多种点云学习任务（即分类、部件分割与语义分割）中均具有有效性。