Fitting boxes to Manhattan scenes using linear integer programming
Type
ArticleKAUST Department
Visual Computing Center (VCC)Date
2016-02-19Online Publication Date
2016-02-19Print Publication Date
2016-08-02Permanent link to this record
http://hdl.handle.net/10754/600705
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Show full item recordAbstract
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 EarthPublisher
Informa UK LimitedAdditional Links
http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1143982ae974a485f413a2113503eed53cd6c53
10.1080/17538947.2016.1143982