Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration
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 mu...
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doaj-0dba6cada3114ed48454480a0ea946952020-11-25T03:52:17ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-10-01964764710.3390/ijgi9110647Large Common Plansets-4-Points Congruent Sets for Point Cloud RegistrationCedrique Fotsing0Nafissetou Nziengam1Christophe Bobda2Faculty 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, GermanyFaculty 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, GermanyDepartment of Electrical and Computer Engineering, University of Florida, 36A Larsen Hall, Gainesville, FL 116200, USAPoint 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.https://www.mdpi.com/2220-9964/9/11/647point cloudsregistrationsegmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cedrique Fotsing Nafissetou Nziengam Christophe Bobda |
spellingShingle |
Cedrique Fotsing Nafissetou Nziengam Christophe Bobda Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration ISPRS International Journal of Geo-Information point clouds registration segmentation |
author_facet |
Cedrique Fotsing Nafissetou Nziengam Christophe Bobda |
author_sort |
Cedrique Fotsing |
title |
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration |
title_short |
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration |
title_full |
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration |
title_fullStr |
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration |
title_full_unstemmed |
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration |
title_sort |
large common plansets-4-points congruent sets for point cloud registration |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2020-10-01 |
description |
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. |
topic |
point clouds registration segmentation |
url |
https://www.mdpi.com/2220-9964/9/11/647 |
work_keys_str_mv |
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