一种基于边界提取的散乱点云简化方法

Point clouds simplification for extraction of boundary

逆向工程中点云密集而无序,大量的冗余数据为后续的数据处理工作带来困难。本文采用单坐标轴搜索方法快速确定点云的k近邻信息,然后在保留所有边界点的基础上,结合测点的曲率特征以及k邻域内保留点的情况对点云进行非均匀简化。由实验结果表明,该算法对边界点具有较强的识别能力,能够在快速、有效地简化点云数据的同时保持原始特征的信息。

In the reverse engineering,the dense and disorder point cloud bring a large number of redundancy data,which cause difficulties to the follow-up work inevitably.This paper used a single axes searching algorithm to gain the information of k-nearest neighbors of the point cloud.And a non-uniform simplification approach,on the basis of all boundary points reserved,simplifies the other points according to the curvature and the proportion of reserved points in their k-nearest neighbors.The experimental result shows that this approach has a strong ability for distinguishing boundary points,and can reduce data directly and effectively while keeping the original features.