综合多种算法的点云精简优化策略与实验研究

Point Cloud Simplification Optimization Strategy and Experimental Research Based on Multiple Algorithms

针对野外扫描原始点云中存在各种形态噪声点和大量冗余数据,提出一种基于方法库、布料模拟滤波和曲率分级等综合算法运用的点云精简优化策略。首先利用统计滤波去除远距离稀疏的噪声点,然后利用直通滤波分割出含有近距离大密度噪声点的点云块,利用布料模拟滤波算法去除这类噪声点,再利用半径滤波去除目标点云周围近距离的噪声点,最后基于曲率分级压缩方法实现对点云冗余数据的去除,并与两种传统的压缩方法进行实验对比分析。实验结果表明,所提的精简优化策略能有效去除点云中的噪声点,在保留点云大部分特征点的同时,能最大化减少点云数据的冗余量,提高了点云模型重建的数据质量。

Aiming at the existence of various morphological noise points and a large amount of redundant data in the original point cloud scanned in the field,this paper proposes a simplification optimization strategy for point clouds based on comprehensive algorithms such as method library,cloth simulation filtering,and curvature classification.First,sparse noise points at long distances are removed by statistical filter.Second,passthrough filter is used to segment point cloud blocks with close distances and large density,and cloth simulation filtering algorithm is used to remove such noise points,and then using radius filter to remove the close distance noise points around the target point cloud.Finally,the redundant data of the point cloud is removed based on curvature-grading compression method and compared with two traditional compression methods for experimental comparison and analysis.Experimental results show that the simplification optimization strategy proposed in this paper can effectively remove the noise points in the point cloud,while retaining most of the characteristic points of the point cloud,it can minimize the redundancy of the point cloud data and improve the data quality of point cloud model reconstruction.