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    Chapter Visibility Enhancement of Crane Operators Using BIM-Based Diminished Reality

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    Author(s)
    Eskandari, Roghieh
    Motamedi, Ali
    Language
    English
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    Abstract
    The limited visibility experienced by crane operators in construction sites poses significant challenges, leading to reduced performance and safety concerns. Obstructive elements, such as existing buildings, construction elements, or vehicles, can block the crane operator's field of view, hindering their ability to execute lifting operations with precision and confidence. To address this issue, this study presents a novel approach using Building Information Modelling (BIM)-based diminished reality (DR) to enhance visibility by dynamically removing obstructive objects from the crane operator's perspective in real-time. The research employs a marker-based registration system that effectively aligns BIM data with the physical environment, ensuring realistic and precise DR visualization. Additionally, a semi-automatic selection method that involves minimal intervention from the user is employed to select desired objects. To generate the background, the system utilizes real-time observation data from occluded areas. A validation through a case study demonstrates the practical applicability of the developed system in real-life construction scenarios
    URI
    https://library.oapen.org/handle/20.500.12657/89116
    Keywords
    Diminished Reality (DR); Augmented Reality (AR); Mixed Reality (MR); Crane; Visibility; Building Information Modelling (BIM); HoloLens; Construction industry
    DOI
    10.36253/979-12-215-0289-3.16
    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
    8
    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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