自适应最优邻域尺寸选择的点云法向量估计方法

A new method of normal estimation for point cloud based on adaptive optimal neighborhoods

为了削弱邻域尺寸选择对基于主成分分析(PCA)的点云法向量估计精度的影响,自适应处理尖锐特征点云,该文提出了自适应邻域的PCA点云法向量估计方法,利用点云局部邻域协方差矩阵,构建了局部邻域维度特征信息熵函数,根据熵函数最小准则,实现了点云自适应最优邻域的估计,在此基础上进行PCA法向量估计。分别对模拟点云和实测点云进行了法向量估计实验。实验结果表明,该文方法能够显著提高包含尖锐特征的点云法向量估计精度。

In order to eliminate the effect of a fixed neighborhood size on the results of normal estimation based on principal component analysis(PCA),and deal with sharp features in point cloud,a new method using an adaptive optimal neighborhood was proposed.For one point in point clouds,a covariance matrix was first constructed by involving its neighbors,the establishment of the entropy function based on the dimensionality features came next,the optimal neighborhood could be determined adaptively by minimizing the value of the entropy function,and the normal vector was finally estimated using the PCA method and the optimal neighborhood obtained just now.Two set of experiments had been conducted using simulated point clouds and scanned ones,respectively,and the results demonstrate that the proposed approach could improve the precision of normal estimation for the point cloud including sharp features.