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Type
ArticleKAUST Department
Computer Science ProgramVisual Computing Center (VCC)
Date
2015-07-26Online Publication Date
2015-07-26Print Publication Date
2016-02Permanent link to this record
http://hdl.handle.net/10754/567059
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Show full item recordAbstract
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 & GraphicsPublisher
Elsevier BVJournal
Computers & GraphicsAdditional Links
http://linkinghub.elsevier.com/retrieve/pii/S0097849315001077ae974a485f413a2113503eed53cd6c53
10.1016/j.cag.2015.07.004