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Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images

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Abstract

As-built building information model (BIM) is an urgent need of the architecture, engineering, construction and facilities management (AEC/FM) community. However, its creation procedure is still labor-intensive and far from maturity. Taking advantage of prevalence of digital cameras and the development of advanced computer vision technology, the paper proposes to reconstruct a building facade and recognize its surface materials from images taken from various points of view. These can serve as initial steps towards automatic generation of as-built BIM. Specifically, 3D point clouds are generated from multiple images using structure from motion method and then segmented into planar components, which are further recognized as different structural components through knowledge based reasoning. Windows are detected through a multilayered complementary strategy by combining detection results from every semantic layer. A novel machine learning based 3D material recognition strategy is presented. Binary classifiers are trained through support vector machines. Material type at a given 3D location is predicted by all its corresponding 2D feature points. Experimental results from three existing buildings validate the proposed system.

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Authors and Affiliations

  1. School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an, 710072, China

    Jun Yang & Zi-Yan Wu

  2. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhong-Ke Shi

Authors
  1. Jun Yang
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  2. Zhong-Ke Shi
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  3. Zi-Yan Wu
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Corresponding author

Correspondence to Jun Yang.

Additional information

This work was supported by National Natural Science Foundation of China (No. 51208425) and Research Foundation of Northwestern Polytechnical University (No. JCY20130127).

Recommended by Associate Editor Jangmyung Lee

Jun Yang received the B. Sc., M. Sc. and Ph.D. degrees in biomedical engineering, traffic information, control engineering and transportation engineering from Northwestern Polytechnical University, China in 2005, 2008 and 2011, respectively. From 2008 to 2010, she was a visiting student and research assistant at Georgia Institute of Technology, USA. She has been an assistant professor in School of Mechanics, Civil Engineering and Architecture in Northwestern Polytechnical University, China, ever since 2011. She has published more than 20 refereed journal and conference papers.

Her research interests include computer vision and applications in civil engineering.

ORCID iD: 0000-0001-5472-2859

Zhong-Ke Shi received the B. Sc., M. Sc. and Ph. D. degrees in control theory and control engineering from Northwestern Polytechnical University, China in 1981, 1985 and 1994 respectively. From 1991 to 1993, he was an associate professor in School of Automation in Northwestern Polytechnical University, China. He has been a professor in School of Automation in Northwestern Polytechnical University, China, ever since 1993. He has published over 300 refereed journal papers and 8 monographs, which have been cited more than 180 times.

His research interests include modern control theory, nonlinear control, and flight control and navigation.

Zi-Yan Wu received the B. Sc. degree in structural Engineering from Tongji University, China in 1984, the M. Sc. degree in structural mechanics from Xian University of Architecture and Technology, China in 1988, and the Ph.D. degree in management science and engineering from Northwestern Polytechnical University, China in 2006. She joined Northwestern Polytechnical University in 1988 and was promoted as a professor in 1999. From January to July of 2005, she visited University of California, Irvine as a visiting scholar. She has published over 90 refereed journal and conference papers.

Her research interest include structural health monitoring and nondestructive testing (NDT) for civil infrastructures.

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Yang, J., Shi, ZK. & Wu, ZY. Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images. Int. J. Autom. Comput. 13, 338–349 (2016). https://doi.org/10.1007/s11633-016-0965-7

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  • DOI: https://doi.org/10.1007/s11633-016-0965-7

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