摘要
由于三维激光扫描仪获取的点云数据体积大且存在大量冗余,在后期处理时会占用计算机大量的空间和时间成本,因此需要对点云数据进行简化预处理。针对散乱点云数据模型,在保留关键几何特征的前提下,提出了一种层次化的点云简化算法。首先,构造点云模型的长方体包围盒,并将包围盒划分成若干个小立方体栅格,使得每个点都包含在栅格中;然后,计算每一个栅格中各个点的权值,通过对比权值与权阈值来确定该点是否保留,从而删除噪声点,实现点云初始简化;最后,采用基于曲率分级的简化算法实现点云精简化。对公共点云数据模型和文物点云数据模型进行了简化实验,实验结果表明,与随机采样法、均匀网格法及法矢夹角法等算法相比,所提算法具备较好的几何特征保持性能,可以达到较好的点云简化效果,是一种有效的点云简化算法。
Since the volume of point cloud data captured by a threedimensional laser scanner is large and leads to redundancy,occupying a lot of computer space and time cost in the later data processing.Thus,the point cloud data processing must be simplified.A hierarchical point cloud simplification algorithm is proposed on the premise of retaining the key geometric features for aiming at the scattered point cloud data model.First,the point cloud model’s cuboid bounding box was constructed and divided into multiple small cube grids,so that each point was contained in the grid.Further,the weight of each point in each grid was estimated,and whether the point was preserved or not was determined by comparing the weight and weight threshold,to eliminate the noise points and achieve the point cloud’s initial simplification.Finally,the simplification algorithm based on curvature classification was employed to achieve the point cloud’s fine simplification.Through the simplification experiments of the common and cultural relic point cloud data model,the results demonstrate that,when compared with the random sampling,uniform grid,and normal vector angle approach,the algorithm has better geometric feature preservation performance,and can achieve better point cloud simplification effect that is an effective point cloud simplification algorithm.
作者
赵夫群
汤慧
Zhao Fuqun;Tang Hui(School of Information,Xi’an University of Finance and Economics,Xi’an 710100,Shaanxi,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第18期213-219,共7页
Laser & Optoelectronics Progress
基金
陕西省自然科学基础研究计划(2021JQ765)
陕西省哲学社会科学重大理论与现实问题研究项目(2021ND0141)。
关键词
成像系统
点云简化
包围盒
权值
曲率分级
简化率
imaging systems
point cloud simplification
bounding box
weight
curvature grading
simplification rate
