【摘要】 针对传统方法在三维点云边缘或细节特征处法向量估计不准确的问题，采用主成分分析、层次聚类和迭代最小二乘法对点云法向量进行估计。通过主成分分析实现点云初始法向量的计算；使用高斯映射和层次聚类算法计算点云中各点的特征系数；利用迭代最小二乘法对初始法向量进行调整。实验结果表明，基于迭代最小二乘的法向量估计方法能对具有边缘或细节特征的三维点云的法向量进行有效估计，能够降低估计结果的根均方误差和估计错误的点的数量，而且可以有效地抑制外点噪声的影响。更多还原

【Abstract】 In order to solve the problem that the normal vector estimation of traditional methods is not accurate at the edge or detail features of 3D point cloud, principal component analysis, hierarchical clustering and iterative least squares are used to estimate the normal vector of point cloud. The initial normal vector of point cloud is calculated by Principal component analysis; Gaussian mapping and Hierarchical clustering algorithm are used to calculate the characteristic coefficients of each point in the point cloud; The initial normal vector is adjusted by iterative least square method. The experimental results show that the normal vector estimation method based on iterative least squares can effectively estimate the normal vector of three-dimensional point cloud with edge or detail features, reduce the root mean square error of the estimation result and the number of wrong points, and effectively suppress the influence of external noise point.更多还原