Paper
27 June 2023 Global accurate multi-view point cloud registration based on manifold clustering and thermal gradient method
Hui Chen, Yibo Wang, Heping Huang, Muhammad Ilyas Menhas
Author Affiliations +
Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127052C (2023) https://doi.org/10.1117/12.2679999
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China
Abstract
Distances are the basic measures used in data clustering. The selection of the distance has a significant impact on the final result. Euclidean distances do not accurately reflect the actual geometrical properties of complex shape and structure, while the geodesic distance may give full consideration to their geometry. Therefore, in the registration process, geodesic distance is more appropriate in such cases. In this study, a multi-view point cloud registration method is proposed to solve the problem of low registration accuracy of objects with complex shape and structure. The method transforms the registration problem into a clustering problem, and the rigid transformation is updated while updating the centroids. Clustering and rigid transformation are alternately applied to the point cloud to achieve final registration results. The proposed method is validated by experiments using data from Stanford University's public datasets. Experimental results show better accuracy and robustness of the proposed method.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hui Chen, Yibo Wang, Heping Huang, and Muhammad Ilyas Menhas "Global accurate multi-view point cloud registration based on manifold clustering and thermal gradient method", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127052C (27 June 2023); https://doi.org/10.1117/12.2679999
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KEYWORDS
Point clouds

Matrices

Image registration

3D modeling

Mixtures

Statistical modeling

Data modeling

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