Multispectral point cloud superpoint segmentation

作     者：WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng 

作者机构：Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies ApplicationKunming 650500China School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbin 150001China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2024年第67卷第4期

页      面：1270-1281页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081002[工学-信号与信息处理] 

基　　金：supported by the Youth Project of the National Natural Science Foundation of China(Grant No.62201237) the Yunnan Fundamental Research Projects(Grant Nos.202101BE070001-008 and202301AV070003) the Youth Project of the Xingdian Talent Support Plan of Yunnan Province(Grant No.KKRD202203068) the Major Science and Technology Projects in Yunnan Province(Grant No.202202AD080013) 

主　　题：multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric 

摘      要：The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.