Fitting Boxes to Manhattan Scenes Using Linear Integer Programming

Minglei Li Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China Liangliang Nan KAUST, KSA Shaochuang Liu Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China

Geometric Modeling and Processing mathscidoc:1608.16113

International Journal of Digital Earth, 9, (8), 2016
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 ts 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.
Urban building models, point cloud, Manhattan scenes, linear integer programming
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@inproceedings{minglei2016fitting,
  title={Fitting Boxes to Manhattan Scenes Using Linear Integer Programming},
  author={Minglei Li, Liangliang Nan, and Shaochuang Liu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829142404615296540},
  booktitle={International Journal of Digital Earth},
  volume={9},
  number={8},
  year={2016},
}
Minglei Li, Liangliang Nan, and Shaochuang Liu. Fitting Boxes to Manhattan Scenes Using Linear Integer Programming. 2016. Vol. 9. In International Journal of Digital Earth. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829142404615296540.
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