Oğuz, EcenurDoğan, YalımGüdükbay, UğurKaraşan, OyaPınar, Mustafa2025-02-282025-02-282024-03-190932-8092https://hdl.handle.net/11693/116988Point 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 a quantile 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 the performances 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 complementing our 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.EnglishCC BY 4.0 DEED (Attribution 4.0 International)https://creativecommons.org/licenses/by/4.0/Point cloud registrationFast point feature histograms (FPFH) descriptorQuantile assignmentIterative closest point algorithmBipartite graph matchingHungarian algorithmHopcroft–Karp algorithmArticle10.1007/s00138-024-01517-31432-1769