Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes
3D spatial data is increasingly employed to generate Building Information Models (BIMs) by extension digital twins for various applications in the architecture, engineering, and construction (AEC) sector such as project monitoring, engineering analyses, retrofit planning, etc. The outputted models o...
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| Formato: | Online |
| Idioma: | inglês |
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Firenze University Press
2024
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| Acesso em linha: | ONIX_20240402_9791221502893_100 |
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| _version_ | 1869515726948139008 |
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| author | Bueno Esposito, Martin Malihi, Shirin Bosche, Frederic |
| author_browse | Bosche, Frederic Bueno Esposito, Martin Malihi, Shirin |
| author_facet | Bueno Esposito, Martin Malihi, Shirin Bosche, Frederic |
| author_sort | Bueno Esposito, Martin |
| collection | Directory of Open Access Books |
| description | 3D spatial data is increasingly employed to generate Building Information Models (BIMs) by extension digital twins for various applications in the architecture, engineering, and construction (AEC) sector such as project monitoring, engineering analyses, retrofit planning, etc. The outputted models of Scan-to-BIM processes should satisfy pre-defined levels of quality. In the case of emerging automated Scan-to-BIM solutions, users however currently need to check all generated geometry manually, which is time-consuming. What would help users is if the automated systems could also provide a level of confidence in the detection and modelling of each element. In this paper three generic indicators are defined for analysing the reliability of the generated 3D models: Icoverage estimates the portion of the surface of the modelled element that can be explained by the input point cloud. Idistance defines the closeness of the generated element models to the input point cloud. The confidence of the generated 3D local models can be computed by combining the two aforementioned indices. The proposed indicators are assessed using actual examples and comparisons are conducted between automatically generated 3D BIM models and 3D models generated manually by a BIM modeler |
| format | Online |
| id | doab-20.500.12854ir-136876 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1368762024-05-10T04:57:42Z Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes Bueno Esposito, Martin Malihi, Shirin Bosche, Frederic BIM point cloud confidence indoor modelling wall digital twin thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization 3D spatial data is increasingly employed to generate Building Information Models (BIMs) by extension digital twins for various applications in the architecture, engineering, and construction (AEC) sector such as project monitoring, engineering analyses, retrofit planning, etc. The outputted models of Scan-to-BIM processes should satisfy pre-defined levels of quality. In the case of emerging automated Scan-to-BIM solutions, users however currently need to check all generated geometry manually, which is time-consuming. What would help users is if the automated systems could also provide a level of confidence in the detection and modelling of each element. In this paper three generic indicators are defined for analysing the reliability of the generated 3D models: Icoverage estimates the portion of the surface of the modelled element that can be explained by the input point cloud. Idistance defines the closeness of the generated element models to the input point cloud. The confidence of the generated 3D local models can be computed by combining the two aforementioned indices. The proposed indicators are assessed using actual examples and comparisons are conducted between automatically generated 3D BIM models and 3D models generated manually by a BIM modeler 2024-05-10T04:57:41Z 2024-05-10T04:57:41Z 2024-04-02T15:47:28Z 2023 chapter ONIX_20240402_9791221502893_100 2704-5846 https://library.oapen.org/handle/20.500.12657/89131 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136876 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89131/1/9791221502893_113.pdf Firenze University Press 10.36253/979-12-215-0289-3.113 10.36253/979-12-215-0289-3.113 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 10 Florence open access |
| spellingShingle | BIM point cloud confidence indoor modelling wall digital twin thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization Bueno Esposito, Martin Malihi, Shirin Bosche, Frederic Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title | Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title_full | Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title_fullStr | Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title_full_unstemmed | Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title_short | Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes |
| title_sort | chapter quantifying the confidence in models outputted by scan to bim processes |
| topic | BIM point cloud confidence indoor modelling wall digital twin thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| topic_facet | BIM point cloud confidence indoor modelling wall digital twin thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| url | ONIX_20240402_9791221502893_100 |
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