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...
| Autores: | , , , |
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| 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 |
| Sumario: | 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 |
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