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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 15
Hlavní autori: Luo, Chengwen, Li, Dong, Yang, Xuebo, Wang, Cheng, Wu, Xiaoyang, Tang, Hao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 ].
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3421607