基于自适应邻域的点云法向估计算法
Point Cloud Normal Estimation Algorithm Based on Adaptive Neighborhoods
DOI: 10.12677/aam.2026.154131, PDF,   
作者: 于佳鑫*, 张 杰：辽宁师范大学数学学院，辽宁 大连
关键词: 法向估计；自适应邻域；尖锐特征；Normal Estimation； Adaptive Neighborhood； Sharp Features
摘要: 法向量是点云的重要几何属性，能够刻画点云的局部结构。因此快速且准确地估计法向量是非常重要的。在估计点云中某一点的法向时，必然要考虑到其局部邻域。在许多估计点云法向的方法中，使用固定邻域无法满足点云中所有点对于邻域的最佳选择。因此本文提出一种基于自适应邻域的点云法向估计算法，以提高点云法向估计的准确性和可靠性。实验结果表明，将自适应邻域算法应用到多种法向估计方法中，法向估计的质量都有所提升。
Abstract: The normal vector is a crucial geometric attribute of point clouds, capable of characterizing the local geometry of the data. Therefore, rapidly and accurately estimating normals is of great importance. When estimating the normal for a point in a cloud, its local neighborhood must be considered. In many existing normal estimation methods, using a fixed neighborhood fails to meet the optimal selection criteria for all points within the cloud. To address this, this paper proposes a point cloud normal estimation algorithm based on adaptive neighborhoods, aiming to improve the accuracy and reliability of the estimations. Experimental results demonstrate that applying the adaptive neighborhood strategy to various existing normal estimation methods leads to a consistent improvement in the quality of the estimated normals.
文章引用：于佳鑫, 张杰. 基于自适应邻域的点云法向估计算法[J]. 应用数学进展, 2026, 15(4): 14-26. https://doi.org/10.12677/aam.2026.154131

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