Reconstructing Building Mass Models from UAV images

Minglei Li KAUST, KSA Liangliang Nan KAUST, KSA Neil Smith KAUST, KSA Peter Wonka KAUST, KSA

Geometric Modeling and Processing mathscidoc:1608.16112

Computers & Graphics, 54, 2016
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.
urban reconstruction, aerial images, point cloud, Markov random field, graph cut
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  title={Reconstructing Building Mass Models from UAV images},
  author={Minglei Li, Liangliang Nan, Neil Smith, and Peter Wonka},
  booktitle={Computers & Graphics},
Minglei Li, Liangliang Nan, Neil Smith, and Peter Wonka. Reconstructing Building Mass Models from UAV images. 2016. Vol. 54. In Computers & Graphics.
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