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Ecenur Oğuz, Yalım Doğan, Uğur Güdükbay, Oya Karaşan, and Mustafa Pınar. Point Cloud Registration with Quantile Assignment. Machine Vision and Applications, 35(3):Article No. 38, 17 pages, May 2024.
Point cloud registration is a fundamental problem in computer vision. The problem encompasses critical tasks such as feature estimation, correspondence matching, and transformation estimation. The point cloud registration problem can be cast as aquantile matching problem. We refined the quantile assignment algorithm by integrating prevalent feature descriptors and transformation estimation methods to enhance the correspondence between the source and target point clouds. We evaluated theperformances of these descriptors and methods with our approach through controlled experiments on a dataset we constructed using well-known 3D models. This systematic investigation led us to identify the most suitable methods for complementingour approach. Subsequently, we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework. Finally, we tested our framework on indoor and outdoor benchmark datasets and compared our results with state-of-the-art point cloud registration methods.
@Article{OguzDGKP24,
author = {Ecenur O{\^g}uz and
Yal{\i}m Do{\^g}an and
U{\^g}ur G{\"u}d{\"u}kbay and
Oya Kara{\c s}an and
Mustafa P{\i}nar},
title = {Point Cloud Registration with Quantile Assignment},
journal = {Machine Vision and Applications},
volume = {35},
number = {3},
year = {2024},
month = {May},
pages = {Article No. 38, 17 pages},
keywords = {Point cloud registration, Fast point feature histograms (FPFH) descriptor, Quantile assignment,
Iterative closest-point algorithm, Bipartite graph matching, Hungarian algorithm, Hopcroft-Karp algorithm},
abstract = {Point cloud registration is a fundamental problem in computer vision. The problem encompasses
critical tasks such as feature estimation, correspondence matching, and transformation estimation.
The point cloud registration problem can be cast as aquantile matching problem. We refined the
quantile assignment algorithm by integrating prevalent feature descriptors and transformation
estimation methods to enhance the correspondence between the source and target point clouds.
We evaluated theperformances of these descriptors and methods with our approach through controlled
experiments on a dataset we constructed using well-known 3D models. This systematic investigation
led us to identify the most suitable methods for complementingour approach. Subsequently,
we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework.
Finally, we tested our framework on indoor and outdoor benchmark datasets and compared
our results with state-of-the-art point cloud registration methods.},
ee = {https://doi.org/10.1007/s00138-024-01517-3},
bib2html_dl_pdf = "http://www.cs.bilkent.edu.tr/~gudukbay/publications/papers/journal_articles/Oguz_Et_Al_MVA_2024.pdf",
bib2html_pubtype = {Refereed Journal Articles},
bib2html_rescat = {Computer Vision},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
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