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Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration

Faculty 1, Department of Graphic Systems, Institute for Computer Science, Brandenburg University of Technology Cottbus-Senftenberg, Platz der Deutschen Einheit 1, P. O. Box 03046 Cottbus, Germany
Department of Electrical and Computer Engineering, University of Florida, 36A Larsen Hall, Gainesville, FL 116200, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 647;
Received: 10 September 2020 / Revised: 22 October 2020 / Accepted: 27 October 2020 / Published: 29 October 2020
(This article belongs to the Special Issue Virtual 3D City Models)
Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved through spatial transformations that align and merge multiple point clouds into a single globally consistent model. In this paper, we present a new segmentation-based approach for point cloud registration. Our method consists of extracting plane structures from point clouds and then, using the 4-Point Congruent Sets (4PCS) technique, we estimate transformations that align the plane structures. Instead of a global alignment using all the points in the dataset, our method aligns 2-point clouds using their local plane structures. This considerably reduces the data size, computational workload, and execution time. Unlike conventional methods that seek to align the largest number of common points between entities, the new method aims to align the largest number of planes. Using partial point clouds of multiple real-world scenes, we demonstrate the superiority of our method compared to raw 4PCS in terms of quality of result (QoS) and execution time. Our method requires about half the execution time of 4PCS in all the tested datasets and produces better alignment of the point clouds. View Full-Text
Keywords: point clouds; registration; segmentation point clouds; registration; segmentation
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MDPI and ACS Style

Fotsing, C.; Nziengam, N.; Bobda, C. Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration. ISPRS Int. J. Geo-Inf. 2020, 9, 647.

AMA Style

Fotsing C, Nziengam N, Bobda C. Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration. ISPRS International Journal of Geo-Information. 2020; 9(11):647.

Chicago/Turabian Style

Fotsing, Cedrique, Nafissetou Nziengam, and Christophe Bobda. 2020. "Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration" ISPRS International Journal of Geo-Information 9, no. 11: 647.

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