Multispectral point cloud superpoint segmentation  

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

机构地区：[1]Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China [2]Yunnan Key Laboratory of Computer Technologies Application,Kunming 650500,China [3]School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Science China(Technological Sciences)》2024年第4期1270-1281,共12页中国科学（技术科学英文版）

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)。

摘　　要：The multitude of airborne point clouds limits the point cloud processing efficiency.Superpoints are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing efficiency.However,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a superpoint.Such feature inconsistencies degrade the performance of subsequent tasks.Thus,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 superpoints.Following the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed.Experiments 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 errors.The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关 键 词：multispectral point cloud superpoint segmentation OVER-SEGMENTATION spatial-spectral joint metric 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]