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Proceedings Paper

Similarity-based global optimization of buildings in urban scene
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Paper Abstract

In this paper, an approach for the similarity-based global optimization of buildings in urban scene is presented. In the past, most researches concentrated on single building reconstruction, making it difficult to reconstruct reliable models from noisy or incomplete point clouds. To obtain a better result, a new trend is to utilize the similarity among the buildings. Therefore, a new similarity detection and global optimization strategy is adopted to modify local-fitting geometric errors. Firstly, the hierarchical structure that consists of geometric, topological and semantic features is constructed to represent complex roof models. Secondly, similar roof models can be detected by combining primitive structure and connection similarities. At last, the global optimization strategy is applied to preserve the consistency and precision of similar roof structures. Moreover, non-local consolidation is adapted to detect small roof parts. The experiments reveal that the proposed method can obtain convincing roof models and promote the reconstruction quality of 3D buildings in urban scene.

Paper Details

Date Published: 27 October 2013
PDF: 8 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 89190K (27 October 2013); doi: 10.1117/12.2031520
Show Author Affiliations
Quansheng Zhu, Wuhan Univ. (China)
Jing Zhang, Wuhan Univ. (China)
Wanshou Jiang, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

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