基于核密度估计的散乱点云边界特征提取

Boundary Feature Abstraction of Unorganized Points Based on Kernel Density Estimation

摘要:

为获得逆向工程中复杂散乱点云的边界特征，提出了一种基于k邻域点集核密度估计的边界特征识别与提取算法，通过R*树索引结构和动态扩展空心球算法实现样点k邻域点集的快速查询，将查询区域半径作为带宽对点集进行核密度估计，由核密度估计获得反映点集分布的模式点，依据模式点到样点的距离与带宽的比值判别边界点特征。实验结果表明，该算法能够快速、准确提取逆向工程中均匀及非均匀分布的散乱点云的边界特征。

Abstract:

In order to obtain the boundary feature of unorganized points, a method of boundary feature recognition and abstraction was proposed based on the kernel density estimation on k-neighborhood of every sample point. k-neighborhood of a sample point could be acquired quickly based on R*-tree index, and the radius of query area viewed as bandwidth was used to kernel density estimation on the point set consisted of the sample point and its k-neighborhood. In this way, the mode points reflected the distribution of point sets could be obtained. According to the ratio of distance between mode points and sample points to the bandwidh of kernel density estimation, the sample points located on boundary could be recognited and abstracted. The experimental results show that the algorithm can obtain the boundary feature of the unorganized points in uniform or nonuniform distribution exactly and rapidly. 