From point cloud data to building information modelling: an automatic parametric workflow for heritage

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, developmentand the digital construction model into a single project. In the case of a heritage building, the Historic Building I...

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Autores: Andriasyan, Mesrop, Moyano, Juan, Nieto Julián, Juan Enrique, Antón García, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/98619
Acceso en línea:https://hdl.handle.net/11441/98619
https://doi.org/10.3390/rs12071094
Access Level:acceso abierto
Palabra clave:Parametric modelling
Scan-to-BIM
Cultural heritage
Point cloud data
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spelling From point cloud data to building information modelling: an automatic parametric workflow for heritageAndriasyan, MesropMoyano, JuanNieto Julián, Juan EnriqueAntón García, DanielParametric modellingScan-to-BIMCultural heritagePoint cloud dataBuilding Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, developmentand the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial LaserScanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advanceson the parametric modelling from remote sensing point cloud data, which is carried out under theRhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this researchMDPIExpresión Gráfica e Ingeniería en la Edificación2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/98619https://doi.org/10.3390/rs12071094reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)Ingléshttp://dx.doi.org/10.3390/rs12071094info:eu-repo/semantics/openAccessoai:idus.us.es:11441/986192026-06-17T12:51:07Z
dc.title.none.fl_str_mv From point cloud data to building information modelling: an automatic parametric workflow for heritage
title From point cloud data to building information modelling: an automatic parametric workflow for heritage
spellingShingle From point cloud data to building information modelling: an automatic parametric workflow for heritage
Andriasyan, Mesrop
Parametric modelling
Scan-to-BIM
Cultural heritage
Point cloud data
title_short From point cloud data to building information modelling: an automatic parametric workflow for heritage
title_full From point cloud data to building information modelling: an automatic parametric workflow for heritage
title_fullStr From point cloud data to building information modelling: an automatic parametric workflow for heritage
title_full_unstemmed From point cloud data to building information modelling: an automatic parametric workflow for heritage
title_sort From point cloud data to building information modelling: an automatic parametric workflow for heritage
dc.creator.none.fl_str_mv Andriasyan, Mesrop
Moyano, Juan
Nieto Julián, Juan Enrique
Antón García, Daniel
author Andriasyan, Mesrop
author_facet Andriasyan, Mesrop
Moyano, Juan
Nieto Julián, Juan Enrique
Antón García, Daniel
author_role author
author2 Moyano, Juan
Nieto Julián, Juan Enrique
Antón García, Daniel
author2_role author
author
author
dc.contributor.none.fl_str_mv Expresión Gráfica e Ingeniería en la Edificación
dc.subject.none.fl_str_mv Parametric modelling
Scan-to-BIM
Cultural heritage
Point cloud data
topic Parametric modelling
Scan-to-BIM
Cultural heritage
Point cloud data
description Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, developmentand the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial LaserScanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advanceson the parametric modelling from remote sensing point cloud data, which is carried out under theRhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research
publishDate 2020
dc.date.none.fl_str_mv 2020
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/11441/98619
https://doi.org/10.3390/rs12071094
url https://hdl.handle.net/11441/98619
https://doi.org/10.3390/rs12071094
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.3390/rs12071094
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 MDPI
publisher.none.fl_str_mv MDPI
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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