- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes
- Shirin Malihi
- Frederic Bosche
- Martin Bueno Esposito
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.113
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
- Keywords:
- BIM,
- point cloud,
- confidence,
- indoor modelling,
- wall,
- digital twin,
University of Edinburgh, United Kingdom - ORCID: 0000-0003-1499-8791
University of Edinburgh, United Kingdom - ORCID: 0000-0002-4064-8982
University of Edinburgh, United Kingdom
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Chapter Information
Chapter Title
Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes
Authors
Shirin Malihi, Frederic Bosche, Martin Bueno Esposito
DOI
10.36253/979-12-215-0289-3.113
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Book Subtitle
Managing the Digital Transformation of Construction Industry
Editors
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/979-12-215-0289-3
eISBN (pdf)
979-12-215-0289-3
eISBN (xml)
979-12-215-0257-2
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846