基于多判别参数混合方法的散乱点云特征提取

针对以往散乱点云特征提取算法存在尖锐特征点提取不完整以及无法保留模型边界点的问题，提出了一种多个判别参数混合方法的特征提取算法。首先，对点云构建k-d tree，利用k-d tree建立点云k邻域；然后，针对每个k邻域计算数据点曲率、点法向与邻域点法向夹角的平均值、点到邻域重心的距离、点到邻域点的平均距离；最后，据此四个参数定义特征阈值和特征判别参数，特征判别参数大于阈值的点即为特征点。实验结果表明，与已有算法相比，该算法不仅可以有效提取尖锐特征点，而且能够识别边界点。

Feature point extraction of scattered point cloud based on multiple parameters hybridization method

This paper proposed an algorithm of extracting feature points based on multiple parameters hybridization method, which aimed to solve the problem that previous algorithms existed, including sharp feature points extraction were incomplete, and could not retain boundary points. Firstly, this algorithm constructed a k-d tree to establish k-nearest neighborhood of the point cloud. Then, it calculated the data point curvature, average vector angle between the point and its k-nearest neighborhood points, distance from point to its neighborhood gravity center, average distance from point to its neighborhood points for each k-nearest neighborhood. Finally, according to the four parameters, it defined the characteristic threshold and feature discriminant parameter. A point recognized as the feature point when its value of discriminant parameter was bigger than the threshold. Compared with existing algorithms, the proposed algorithm can not only extract the steep feature points, but also have the ability to identify the boundary points.