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    Fitting boxes to Manhattan scenes using linear integer programming

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    BoxFitting.pdf
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    Type
    Article
    Authors
    Li, Minglei
    Nan, Liangliang cc
    Liu, Shaochuang
    KAUST Department
    Visual Computing Center (VCC)
    Date
    2016-02-19
    Online Publication Date
    2016-02-19
    Print Publication Date
    2016-08-02
    Permanent link to this record
    http://hdl.handle.net/10754/600705
    
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    Abstract
    We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption. The method first aligns the point cloud with a per-building local coordinate system, and then fits axis-aligned planes to the point cloud through an iterative regularization process. The refined planes partition the space of the data into a series of compact cubic cells (candidate boxes) spanning the entire 3D space of the input data. We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation. The objective function is designed to maximize the point cloud coverage and the compactness of the final model. Finally, all selected boxes are merged into a lightweight polygonal mesh model, which is suitable for interactive visualization of large scale urban scenes. Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.
    Citation
    Fitting boxes to Manhattan scenes using linear integer programming 2016:1 International Journal of Digital Earth
    Publisher
    Informa UK Limited
    Journal
    International Journal of Digital Earth
    DOI
    10.1080/17538947.2016.1143982
    Additional Links
    http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1143982
    ae974a485f413a2113503eed53cd6c53
    10.1080/17538947.2016.1143982
    Scopus Count
    Collections
    Articles; Visual Computing Center (VCC)

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