Point cloud optimization based on 3D geometric features for architectural heritage modelling

The present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing step for...

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Detalhes bibliográficos
Autores: Rodríguez Gonzálvez, Pablo, Jiménez Fernández-Palacios, Belén
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/160439
Acesso em linha:https://hdl.handle.net/11441/160439
https://doi.org/10.20365/disegnarecon.26.2021.18
Access Level:acceso abierto
Palavra-chave:Classification
Optimization
Cultural heritage
Point cloud
Geometrical features
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spelling Point cloud optimization based on 3D geometric features for architectural heritage modellingRodríguez Gonzálvez, PabloJiménez Fernández-Palacios, BelénClassificationOptimizationCultural heritagePoint cloudGeometrical featuresThe present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing step for architectural heritage modelling. A point cloud classification that optimizes the point cloud while preserving the relevant information will improve the subsequent operations. The present methodology is based on the extraction of the geometric properties of the 3D point clouds on the basis of the 3D covariance matrix. Among all the possible dimensional features, the omnivariance (Ω) is considered the most suitable for the variety of situations of the architectural heritage elements. For a study case of the Niculoso Pisano Portal of the Monastery of Santa Paula of Seville (Spain), three clusters are defined according to the different level of details. As a result, and in comparison, to a standard spatial sampling of 1 cm, the proposed clustering allowed a weight spatial sampling within the interval 20 – 1 cm, achieving an 85%-point reduction, keeping 3D points in the complex areas, whereas the low detail areas, like planes, were considerably reduced in size for the next steps of parametric modelling. The error of the optimized point cloud, by the comparison with the original point cloud has a mean value of 0.3 mm and a standard deviation of ± 4.6 mm.University of L'AquilaIngeniería Gráfica2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/160439https://doi.org/10.20365/disegnarecon.26.2021.18reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésDisegnarecon, 14 (26), 18.1-18.9.https://disegnarecon.univaq.it/ojs/index.php/disegnarecon/article/view/811/495info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1604392026-06-17T12:51:07Z
dc.title.none.fl_str_mv Point cloud optimization based on 3D geometric features for architectural heritage modelling
title Point cloud optimization based on 3D geometric features for architectural heritage modelling
spellingShingle Point cloud optimization based on 3D geometric features for architectural heritage modelling
Rodríguez Gonzálvez, Pablo
Classification
Optimization
Cultural heritage
Point cloud
Geometrical features
title_short Point cloud optimization based on 3D geometric features for architectural heritage modelling
title_full Point cloud optimization based on 3D geometric features for architectural heritage modelling
title_fullStr Point cloud optimization based on 3D geometric features for architectural heritage modelling
title_full_unstemmed Point cloud optimization based on 3D geometric features for architectural heritage modelling
title_sort Point cloud optimization based on 3D geometric features for architectural heritage modelling
dc.creator.none.fl_str_mv Rodríguez Gonzálvez, Pablo
Jiménez Fernández-Palacios, Belén
author Rodríguez Gonzálvez, Pablo
author_facet Rodríguez Gonzálvez, Pablo
Jiménez Fernández-Palacios, Belén
author_role author
author2 Jiménez Fernández-Palacios, Belén
author2_role author
dc.contributor.none.fl_str_mv Ingeniería Gráfica
dc.subject.none.fl_str_mv Classification
Optimization
Cultural heritage
Point cloud
Geometrical features
topic Classification
Optimization
Cultural heritage
Point cloud
Geometrical features
description The present article shows a novel methodology to classify 3D point clouds related to architectural heritage elements based on dimensional features, and using open software. The 3D point cloud is the key element for the extraction of semantic and/or vector information, as well as the meshing step for architectural heritage modelling. A point cloud classification that optimizes the point cloud while preserving the relevant information will improve the subsequent operations. The present methodology is based on the extraction of the geometric properties of the 3D point clouds on the basis of the 3D covariance matrix. Among all the possible dimensional features, the omnivariance (Ω) is considered the most suitable for the variety of situations of the architectural heritage elements. For a study case of the Niculoso Pisano Portal of the Monastery of Santa Paula of Seville (Spain), three clusters are defined according to the different level of details. As a result, and in comparison, to a standard spatial sampling of 1 cm, the proposed clustering allowed a weight spatial sampling within the interval 20 – 1 cm, achieving an 85%-point reduction, keeping 3D points in the complex areas, whereas the low detail areas, like planes, were considerably reduced in size for the next steps of parametric modelling. The error of the optimized point cloud, by the comparison with the original point cloud has a mean value of 0.3 mm and a standard deviation of ± 4.6 mm.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/160439
https://doi.org/10.20365/disegnarecon.26.2021.18
url https://hdl.handle.net/11441/160439
https://doi.org/10.20365/disegnarecon.26.2021.18
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Disegnarecon, 14 (26), 18.1-18.9.
https://disegnarecon.univaq.it/ojs/index.php/disegnarecon/article/view/811/495
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv University of L'Aquila
publisher.none.fl_str_mv University of L'Aquila
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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