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Uniaxial Partitioning Strategy for Efficient Point Cloud Registration
In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. In this work, we introduce a cloud-partitioning str...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030376/ https://www.ncbi.nlm.nih.gov/pubmed/35458872 http://dx.doi.org/10.3390/s22082887 |
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author | Souza Neto, Polycarpo Marques Soares, José Pereira Thé, George André |
author_facet | Souza Neto, Polycarpo Marques Soares, José Pereira Thé, George André |
author_sort | Souza Neto, Polycarpo |
collection | PubMed |
description | In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. In this work, we introduce a cloud-partitioning strategy for improved registration and compare it to other relevant approaches by using both time and quality of pose correction. Quality is assessed from a rotation metric and also by the root mean square error (RMSE) computed over the points of the source cloud and the corresponding closest ones in the corrected target point cloud. A wide and plural set of experimentation scenarios was used to test the algorithm and assess its generalization, revealing that our cloud-partitioning approach can provide a very good match in both indoor and outdoor scenes, even when the data suffer from noisy measurements or when the data size of the source and target models differ significantly. Furthermore, in most of the scenarios analyzed, registration with the proposed technique was achieved in shorter time than those from the literature. |
format | Online Article Text |
id | pubmed-9030376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90303762022-04-23 Uniaxial Partitioning Strategy for Efficient Point Cloud Registration Souza Neto, Polycarpo Marques Soares, José Pereira Thé, George André Sensors (Basel) Article In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. In this work, we introduce a cloud-partitioning strategy for improved registration and compare it to other relevant approaches by using both time and quality of pose correction. Quality is assessed from a rotation metric and also by the root mean square error (RMSE) computed over the points of the source cloud and the corresponding closest ones in the corrected target point cloud. A wide and plural set of experimentation scenarios was used to test the algorithm and assess its generalization, revealing that our cloud-partitioning approach can provide a very good match in both indoor and outdoor scenes, even when the data suffer from noisy measurements or when the data size of the source and target models differ significantly. Furthermore, in most of the scenarios analyzed, registration with the proposed technique was achieved in shorter time than those from the literature. MDPI 2022-04-09 /pmc/articles/PMC9030376/ /pubmed/35458872 http://dx.doi.org/10.3390/s22082887 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Souza Neto, Polycarpo Marques Soares, José Pereira Thé, George André Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title | Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title_full | Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title_fullStr | Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title_full_unstemmed | Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title_short | Uniaxial Partitioning Strategy for Efficient Point Cloud Registration |
title_sort | uniaxial partitioning strategy for efficient point cloud registration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030376/ https://www.ncbi.nlm.nih.gov/pubmed/35458872 http://dx.doi.org/10.3390/s22082887 |
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