一种利用矢量-角度法的模型点云压缩算法
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摘要
针对点云数据过密、冗余信息较多的问题,提出了一种基于自适应切片与矢量-角度法的点云快速压缩算法,该算法利用包围盒将点云进行自适应分层形成切片点云,然后对每层点云使用矢量-角度法精简数据。利用该算法开展了试验研究,通过试验分别讨论分层数目、最短距离和角度参数对压缩结果的影响,并通过构建网格模型验证压缩效果。试验结果表明:该算法对目标特征复杂的部位有较好的压缩效果,在平坦部位不会因过度压缩出现失真现象;该算法能够自适应地保留反映目标特征和细节的点,实现点云的快速压缩。
Aiming at the problem of overdense and redundant information of scattered point cloud data,this paper proposes a fast compression algorithm of scattered point cloud based on adaptive slice and vector angle method,which uses slicing technique to stratify point cloud into a slice point cloud.Then,the vector angle method is used to simplify the data for each point cloud.This paper uses this algorithm to carry out experimental research,discusses the influence of the shortest distance and angle parameters on the compression result,and verifies the compression effect by constructing the mesh model.The result shows the algorithm has better compression effect on complex target features realizing fast compression of point cloud. In the meantime,there is no distortion due to excessive compression.The algorithm can adapt the adaptive reservation to reflect the target feature and the detail point,and realize the rapid compression of point cloud.
Aiming at the problem of overdense and redundant information of scattered point cloud data,this paper proposes a fast compression algorithm of scattered point cloud based on adaptive slice and vector angle method,which uses slicing technique to stratify point cloud into a slice point cloud.Then,the vector angle method is used to simplify the data for each point cloud.This paper uses this algorithm to carry out experimental research,discusses the influence of the shortest distance and angle parameters on the compression result,and verifies the compression effect by constructing the mesh model.The result shows the algorithm has better compression effect on complex target features realizing fast compression of point cloud. In the meantime,there is no distortion due to excessive compression.The algorithm can adapt the adaptive reservation to reflect the target feature and the detail point,and realize the rapid compression of point cloud.
引文
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[2]徐景中,万幼川,张圣望.LiDAR地面点云的简化方法研究[J].测绘信息与工程,2008,33(1):32-34.
[3]张德海,崔国英,白代萍,等.逆向工程中的三维光学检测点云采样技术研究[J].计算机应用研究,2014,31(3):946-948.
[4]焦向东,佟泽民.分层制造法的材料技术及其发展[J].中国机械工程,2000,11(5):582-584.
[5]邢正全,邓喀中,薛继群.基于栅格划分和法向量估计的点云数据压缩[J].测绘通报,2012(7):50-52.
[6]王峰.集成RTK的三维激光扫描技术测量地形的方法[J].测绘通报,2017(3):71-75.
[7]郑德华.点云数据直接缩减方法及缩减效果研究[J].测绘工程,2006,15(4):27-23.
[8]杨璐璟.点云数据的压缩算法研究[D].长沙:中南大学,2014.
[9]吴杭彬,刘春.激光扫描数据的等值线分层提取和多细节表达[J].同济大学学报(自然科学版),2009,37(2):276-271.
[10] RIANMORA S,KOOMSAP P,DANG PHI V H.Selective Data Acquisition for Direct Integration of Reverse Engineering and Rapid Prototyping[J]. Virtual and Physical Prototyping,2009,4(4):227-239.
[11]李凤霞,饶永辉,刘陈,等.基于法向夹角的点云数据精简算法[J].系统仿真学报,2012,24(9):1980-1983.
[12]李仁忠,杨曼,刘阳阳,等.一种散乱点云的均匀精简算法[J].光学学报,2017,37(7):97-105.
[13]任乃飞,胡汝霞,万俊.点云自适应切片方法研究[J].农业机械学报,2006,37(2):118-121.
[14]方芳,程效军.海量散乱点云快速压缩算法[J].武汉大学学报(信息科学版),2013,38(11):1353-1357.
[15]王亚美,赵萍.一种基于点云数据的直接分层算法[J].沈阳理工大学学报,2009,28(3):39-41.