Three-dimensional building reconstruction using highly overlapped aerial images

Tee-Ann Teo; Liang-Chien Chen; Hong-Kuei Cheng; Shin-Hui Lee

doi:10.1117/12.832546

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30 October 2009 Three-dimensional building reconstruction using highly overlapped aerial images

Tee-Ann Teo, Liang-Chien Chen, Hong-Kuei Cheng, Shin-Hui Lee

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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960D (2009) https://doi.org/10.1117/12.832546
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The development of digital aerial camera provides the possibility of acquiring highly overlapped aerial images with high spatial resolution. In addition to its high spatial resolution that improves the capability of image interpretation, the highly overlapped images provide favorable geometrical configuration with high redundancy. The high similarity of stereo images is, thus, beneficial to the reliable image matching. Hence, the 3-D point clouds from the image matching have the great potential in 3-D modeling. The topographic maps provide the distinct boundaries for building modeling. The strategies of building reconstruction with existing topographic maps may improve the quality, cost, and efficiency for building modeling. The objective of this investigation is to integrate highly overlapped aerial images and building boundaries from topographic map. The proposed semi-automatic method includes 3-D lines extraction and polyhedral model generation. In the beginning, the operator locates the initial lines by a graphical user interface. The initial lines are refined by stereo images. The precise 3-D lines are processed into a 3-D modeling by an inference engine. The experimental results indicate that the proposed method may reconstruct the 3-D building model effectively.

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Tee-Ann Teo, Liang-Chien Chen, Hong-Kuei Cheng, and Shin-Hui Lee "Three-dimensional building reconstruction using highly overlapped aerial images", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960D (30 October 2009); https://doi.org/10.1117/12.832546

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