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    Automatic 3D Reconstruction for As-built Underground Utilities

    Su Y 2024 Public.pdf (6.025Mb)
    Access Status
    Open access
    Authors
    Su, Yang
    Date
    2024
    Supervisor
    Brad Carey
    Peng Wu
    Jun Wang
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    Faculty
    Humanities
    School
    School of Design and the Built Environment
    URI
    http://hdl.handle.net/20.500.11937/96628
    Collection
    • Curtin Theses
    Abstract

    This thesis addresses three challenges in the 3D reconstruction of underground utilities. Firstly, it introduces a high-precision ground penetrating radar-based deep learning model to enhance localization accuracy. Secondly, it presents an unsupervised deep learning model for effective 3D reconstruction under low-light conditions. Finally, it proposes a graph convolutional network-based model to accurately complete missing topological data of utility networks. Experimental results demonstrate significant improvements in accuracy, speed, and data integrity.

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