GPSCO: Global Planar Structural Constraint Optimal-Based Point Cloud Registration Algorithm for Repetitive Structures

3-D point cloud registration is a prerequisite for scene reconstruction and 3-D object recognition in computer vision and remote sensing. Numerous previous studies have presented a series of point cloud registration algorithms with diverse efficiencies and accuracies. However, registering point clou...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 15
Hlavní autoři:	Luo, Chengwen, Li, Dong, Yang, Xuebo, Wang, Cheng, Wu, Xiaoyang, Tang, Hao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	3-D point cloud Algorithms Benchmark testing Clustering Clustering algorithms Computer vision Datasets Feature extraction Geometric constraints Image reconstruction Image registration Lidar Object recognition Pattern recognition plane constraint Planes Point cloud compression Registration Remote sensing Repetitive structures Structures Three dimensional models Three-dimensional displays Transmission line matrix methods Vectors
ISSN:	0196-2892, 1558-0644
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Abstract	3-D point cloud registration is a prerequisite for scene reconstruction and 3-D object recognition in computer vision and remote sensing. Numerous previous studies have presented a series of point cloud registration algorithms with diverse efficiencies and accuracies. However, registering point clouds with repetitive structures is still challenging. In this study, we propose the global planar structural constraint optimal (GPSCO)-based algorithm, which is specifically designed to handle the registration of repetitive structures. Its novelty lies in establishing the geometric constraint of multiple planes and registering based on the global optimal geometric constraint. The specific algorithm involves clustering the parallel planes into plane groups, estimating matching scores between plane groups, and selecting three corresponding pairs of nonparallel plane groups to form the plane structural constraint. The transformation matrix determined in the case of the optimal structural constraint is taken as the final result. The two terrestrial LiDAR datasets (HS1 and HS2) of real scenes with repetitive structures were collected to evaluate the GPSCO algorithm. Additionally, the GPSCO algorithm is validated on four public benchmark datasets, such as Whu-Park, Whu-Campus, ETH-Hauptgebaude, and ETH-Stairs. The registration results demonstrate that the GPSCO algorithm achieves 95.65%, 86.36%, 100%, 100%, 100%, and 88.89% successful registration rates (SRRs) on the six datasets, respectively, and significantly outperforms the existing methods on HS1 and HS2 with repetitive structures. The corresponding datasets and code are available at [ https://github.com/fog223/GPSCO ].
AbstractList	3-D point cloud registration is a prerequisite for scene reconstruction and 3-D object recognition in computer vision and remote sensing. Numerous previous studies have presented a series of point cloud registration algorithms with diverse efficiencies and accuracies. However, registering point clouds with repetitive structures is still challenging. In this study, we propose the global planar structural constraint optimal (GPSCO)-based algorithm, which is specifically designed to handle the registration of repetitive structures. Its novelty lies in establishing the geometric constraint of multiple planes and registering based on the global optimal geometric constraint. The specific algorithm involves clustering the parallel planes into plane groups, estimating matching scores between plane groups, and selecting three corresponding pairs of nonparallel plane groups to form the plane structural constraint. The transformation matrix determined in the case of the optimal structural constraint is taken as the final result. The two terrestrial LiDAR datasets (HS1 and HS2) of real scenes with repetitive structures were collected to evaluate the GPSCO algorithm. Additionally, the GPSCO algorithm is validated on four public benchmark datasets, such as Whu-Park, Whu-Campus, ETH-Hauptgebaude, and ETH-Stairs. The registration results demonstrate that the GPSCO algorithm achieves 95.65%, 86.36%, 100%, 100%, 100%, and 88.89% successful registration rates (SRRs) on the six datasets, respectively, and significantly outperforms the existing methods on HS1 and HS2 with repetitive structures. The corresponding datasets and code are available at [ https://github.com/fog223/GPSCO ].
Author	Wu, Xiaoyang Wang, Cheng Yang, Xuebo Li, Dong Luo, Chengwen Tang, Hao
Author_xml	– sequence: 1 givenname: Chengwen orcidid: 0009-0000-3505-3936 surname: Luo fullname: Luo, Chengwen email: luochengwen22@mails.ucas.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Dong orcidid: 0009-0005-3206-7917 surname: Li fullname: Li, Dong email: lidong@aircas.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Xuebo orcidid: 0000-0002-0949-0179 surname: Yang fullname: Yang, Xuebo email: yangxb@aircas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Cheng surname: Wang fullname: Wang, Cheng organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Xiaoyang orcidid: 0009-0001-6851-0395 surname: Wu fullname: Wu, Xiaoyang organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 6 givenname: Hao surname: Tang fullname: Tang, Hao organization: Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, China
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SubjectTerms	3-D point cloud Algorithms Benchmark testing Clustering Clustering algorithms Computer vision Datasets Feature extraction Geometric constraints Image reconstruction Image registration Lidar Object recognition Pattern recognition plane constraint Planes Point cloud compression Registration Remote sensing Repetitive structures Structures Three dimensional models Three-dimensional displays Transmission line matrix methods Vectors
Title	GPSCO: Global Planar Structural Constraint Optimal-Based Point Cloud Registration Algorithm for Repetitive Structures
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