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Multispectral point cloud superpoint segmentation

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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 in-formation 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.

multispectral point cloudsuperpoint segmentationover-segmentationspatial-spectral joint metric

WANG QingWang、WANG MingYe、ZHANG ZiFeng、SONG Jian、ZENG Kai、SHEN Tao、GU YanFeng

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Faculty of Information Engineering and Automation,Kunming University of Science and Technology Kunming 650500,China

Yunnan Key Laboratory of Computer Technologies Application,Kunming 650500,China

School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China

国家自然科学基金青年基金Yunnan Fundamental Research ProjectsYunnan Fundamental Research ProjectsYouth Project of the Xingdian Talent Support Plan of Yunnan ProvinceMajor Science and Technology Projects in Yunnan Province

62201237202101BE070001-008202301A V070003KKRD202203068202202AD080013

2024

10.1007/s11431-023-2528-8

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(4)
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