基于特征约束的点云简化算法研究

The Point Cloud Simplification Algorithm Based on Feature Constraint

针对传统的点云简化算法导致特征区域容易丢失的问题,提出了一种新的基于特征约束的点云简化的算法。首先对散乱点云用KD-TREE建立起空间拓扑关系,在此基础上建立起单个点的K-邻域。然后对K-邻域内建立起最小二乘平面,设定合理的阈值来度量数据点的重要性。依据特征点的分布估算每个点的简化距离阈值,以此为基础对每个点进行自适应简化。实验证明该算法能满足在点云数据简化过程中检测并保留特征点的要求。

Point cloud simplification algorithm for traditional lead easily lost by the charaeteristies of regional issues, a new simplified algorithm based on the point cloud feature constraints is presented. At First, KD - TREE establish spatial topological relations of scat- tered point cloud, established on the basis of the K - neighborhood of a single point. Then the K - neighborhood established least squares plane, to set a reasonable threshold value to measure the importance of the data points. Based on the distribution of the feature point to estimate the the simplified threshold distance of each point, as the basis for each point adaptive simplification. Experiments shows that the algorithm can meet in the point cloud data to simplify the process of detection and feature points reserved requirements.