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    Reconstructing building mass models from UAV images

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    urban_aerial.pdf
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    Type
    Article
    Authors
    Li, Minglei
    Nan, Liangliang cc
    Smith, Neil
    Wonka, Peter cc
    KAUST Department
    Computer Science Program
    Visual Computing Center (VCC)
    Date
    2015-07-26
    Online Publication Date
    2015-07-26
    Print Publication Date
    2016-02
    Permanent link to this record
    http://hdl.handle.net/10754/567059
    
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    Abstract
    We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method.
    Citation
    Reconstructing building mass models from UAV images 2016, 54:84 Computers & Graphics
    Publisher
    Elsevier BV
    Journal
    Computers & Graphics
    DOI
    10.1016/j.cag.2015.07.004
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0097849315001077
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.cag.2015.07.004
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC)

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