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    Chapter Quantifying the Confidence in Models Outputted by Scan-To-BIM Processes

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    Author(s)
    Bueno Esposito, Martin
    Malihi, Shirin
    Bosche, Frederic
    Language
    English
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    Abstract
    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
    URI
    https://library.oapen.org/handle/20.500.12657/89131
    Keywords
    BIM; point cloud; confidence; indoor modelling; wall; digital twin
    DOI
    10.36253/979-12-215-0289-3.113
    ISBN
    9791221502893, 9791221502893
    Publisher
    Firenze University Press
    Publisher website
    https://www.fupress.com/
    Publication date and place
    Florence, 2023
    Series
    Proceedings e report, 137
    Classification
    Virtualization
    Pages
    10
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/legalcode
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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