Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level
Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so eff...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-10-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-4-2024/223/2024/isprs-annals-X-4-2024-223-2024.pdf |
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| author | S. Malihi F. Bosché |
| author_facet | S. Malihi F. Bosché |
| author_sort | S. Malihi |
| collection | DOAJ |
| description | Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy. |
| format | Article |
| id | doaj-art-8fc892d092264ed7987bd8f366b176ac |
| institution | OA Journals |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-8fc892d092264ed7987bd8f366b176ac2025-08-20T01:47:58ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502024-10-01X-4-202422322910.5194/isprs-annals-X-4-2024-223-2024Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence LevelS. Malihi0F. Bosché1Civil Engineering Department, University of Cambridge, United KingdomSchool of Engineering, University of Edinburgh, United KingdomScan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy.https://isprs-annals.copernicus.org/articles/X-4-2024/223/2024/isprs-annals-X-4-2024-223-2024.pdf |
| spellingShingle | S. Malihi F. Bosché Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level |
| title_full | Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level |
| title_fullStr | Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level |
| title_full_unstemmed | Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level |
| title_short | Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level |
| title_sort | integrating surface related indicators of coverage distance and distribution for quantifying scan to bim confidence level |
| url | https://isprs-annals.copernicus.org/articles/X-4-2024/223/2024/isprs-annals-X-4-2024-223-2024.pdf |
| work_keys_str_mv | AT smalihi integratingsurfacerelatedindicatorsofcoveragedistanceanddistributionforquantifyingscantobimconfidencelevel AT fbosche integratingsurfacerelatedindicatorsofcoveragedistanceanddistributionforquantifyingscantobimconfidencelevel |