激光点云的稀疏体素金字塔邻域构建与分类  

Sparse voxel pyramid neighborhood construction and classification of LiDAR point cloud

作　　者：陶帅兵 梁冲 蒋腾平 杨玉娇 王永君 Tao Shuaibing;Liang Chong;Jiang Tengping;Yang Yujiao;Wang Yongjun(Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China;Key Laboratory of Virtual Geographic Environment,Ministry of Education,Nanjing Normal University,Nanjing 210023,China;State Key Laboratory Cultivation Base of Geographical Environment Evolution,Nanjing Normal University,Nanjing 210023,China;State Key Laboratory of Information Engineering in Surveying,Mapping,and Remote Sensing,Wuhan University,Wuhan 430072,China)

机构地区：[1]江苏省地理信息资源开发与应用协同创新中心,南京210023 [2]南京师范大学虚拟地理环境教育部重点实验室,南京210023 [3]南京师范大学地理环境演变国家重点实验室培育点,南京210023 [4]武汉大学测绘遥感信息工程国家重点实验室,武汉430072

出　　处：《中国图象图形学报》2021年第11期2703-2712,共10页Journal of Image and Graphics

基　　金：国家自然科学基金项目(41771439);国家重点研发计划项目(2016YFB0502304);自然资源部城市国土资源监测与仿真重点实验室项目(KF-2018-03-070)。

摘　　要：目的在点云分类处理的各环节中,关键是准确描述点云的局部邻域结构并提取表达能力强的点云特征集合。为了改进传统邻域结构单尺度特征表达能力的有限性和多尺度特征的计算复杂性,本文提出了用于激光点云分类的稀疏体素金字塔邻域结构及对应的分类方法。方法通过对原始数据进行不同尺度下采样构建稀疏体素金字塔,并根据稀疏体素金字塔提取多尺度特征,利用随机森林分类器进行初始分类;构建无向图,利用直方图交集核计算邻域点之间连接边的权重,通过多标签图割算法优化分类结果。当体素金字塔的接收域增大时,邻域点密度随其距离中心点距离的增加而减小,有效减少了计算量。结果在地基Semantic3D数据集、车载点云数据和机载点云数据上进行实验,结果表明,在降低计算复杂性的前提下,本文方法的分类精度、准确性和鲁棒性达到了同类算法前列,验证了该框架作为点云分类基础框架的有效性。结论与类似方法相比,本文方法提取的多尺度特征既保持了点的局部结构信息,也更好地兼顾了较大尺度的点云结构特征,因而提升了点云分类的精度。Objective Point cloud classification is one of the hotspots of computer vision research.Among of various kinds of processing stages,accurately describing the local neighborhood structure of the point cloud and extracting the point cloud feature sets with strong expressive ability has become the key to point cloud classification.Traditionally,two methods can be used for modeling the neighborhood structure of point clouds:single-scale description and multiscale description.The former has a limited expressive ability,whereas the latter has a strong description ability but comes with a high computational complexity.To solve the above problems,this paper proposes a sparse voxel pyramid structure to express the local neighborhood structure of the point cloud and provides the corresponding point cloud classification and optimization method.Method First,after a comparative analysis of related point cloud classification methods,the paper describes in detail the structure of the proposed sparse voxel pyramid,analyzes the advantages of the sparse voxel pyramid in expressing the neighborhood structure of the point cloud,and provides the method to express the local neighborhood of the point could with this structure.When calculating point features,the influence of candidate points on the local feature calculation results gradually decreases as the distance decreases.Thus,a fixed number of neighbors is used to construct each layer of the sparse voxel pyramid.For each voxel,a sparse voxel pyramid of N layers is constructed,and the voxel radius of the 0th layer is set to R.The value of N can be set according to the computing power of hardware resources.The R value is the smallest voxel value in the entire voxel pyramid,and its size can be set according to the point cloud density and range of the scene.The voxel radius of each subsequent layer of the pyramid is in turn twice that of the previous layer.The voxel radius of the Nth layer is 2 N R,and each layer contains the same number of K voxels.Each point in the original point cl

关 键 词：点云分类 稀疏体素金字塔 多尺度特征 多标签图割 直方图交集核 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]