Abstract
3D building modeling has many potential uses in the fields of construction, city planning and public security. An image-based 3D semantic modeling method of building facade is proposed in this paper. Dense point clouds are generated from inputting images by structure from motion and cluster based multi-view-stereo algorithms. Planar components are extracted from generated point clouds by random sample consensus and further recognized as structural components based on prior knowledge. Windows are detected through a multi-layer complementary strategy with binary image processing techniques. Experimental results from two building facades verify the proposed method.
This work was supported by National Natural Science Foundation of China (No.51208425) and Research Foundation of Northwestern Polytechnical University (No.JCY20130127).
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction 31, 325–337 (2013)
Klein, L., Li, N., Becerik-Gerber, B.: Imaged-based verification of as-built documentation of operational buildings. Automation in Construction 21, 161–171 (2012)
Goldparvar-Fard, M., Peña-Mora, F., Savarese, S.: D4AR-a 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication. Journal of Information Technology in Construction 14, 129–153 (2009)
Son, H., Kim, C.: 3D structural component recognition and modeling method using color and 3D data for construction progress monitoring. Automation in Construction 19, 844–854 (2010)
Pu, S., Vosselman, G.: Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing 64, 575–584 (2009)
Brilakis, I., Lourakis, M., Sacks, R., Savarese, S., Christodoulou, S., Teizer, J., Makhmalbaf, A.: Toward automated generation of parametric BIMs based on hybrid video and laser scanning data. Advanced Engineering Informatics 24, 456–465 (2010)
Bohm, J., Becker, S., Haala, N.: Model refinement by integrated processing of laser scanning and photogrammetry. In: Proceedings of 2nd International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures (3D-Arch) (2007)
Tang, P., Huber, D., Akinci, B., Lipman, R., Lytle, A.: Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. Automation in construction 19, 829–843 (2010)
Martinez, J., Soria-Medina, A., Arias, P., Buffara-Antunes, A.F.: Automatic processing of Terrestrial Laser Scanning data of building facades. Automation in Construction 22, 298–305 (2012)
Gonzalvez, P.R., Aguilera, D.G., Lahoz, J.G.: From point cloud to surface: Modeling structures in laser scanner point clouds. In: ISPRS Workshop on Laser Scanning, pp. 338–344 (2007)
Schnabel, R., Wessel, R., Wahl, R., Klein, R.: Shape recognition in 3d point-clouds. In: Proc. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision, vol. 2 (2008)
Bhatla, A., Choe, S.Y., Fierro, O., Leite, F.: Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras. Automation in construction 28, 116–127 (2012)
Golparvar-Fard, M., Bohn, J., Teizer, J., Savarese, S., Pena-Mora, F.: Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques. Automation in Construction 20, 1143–1155 (2011)
Kim, C., Son, H., Kim, C.: The effective acquisition and processing of 3D photogrammetric data from digital photogrammetry for construction progress measurement. In: ASCE International Workshop on Computing in Civil Engineering, pp. 178–185 (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Wu, C.: Towards Linear-time Incremental Structure from Motion
Labatut, P., Pons, J.-P., Keriven, R.: Efficient multi-view reconstruction of large-scale scenes using interest points, delaunay triangulation and graph cuts. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Jancosek, M., Pajdla, T.: Multi-view reconstruction preserving weakly-supported surfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3121–3128 (2011)
Yang, M.Y., Förstner, W.: Plane detection in point cloud data. In: Proceedings of the 2nd Int. Conf. on machine control guidance, vol. 1, pp. 95–104 (2010)
Radopoulou, S.C., Sun, M., Dai, F., Brilakis, I., Savarese, S.: Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection. In: ASCE International Workshop on Computing in Civil Engineering, pp. 309–316 (2012)
Bauer, J., Karner, K., Schindler, K., Klaus, A., Zach, C.: Segmentation of building models from dense 3D point-clouds. In: Proc. 27th Workshop of the Austrian Association for Pattern Recognition, pp. 253–258 (2003)
Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1434–1441 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, J., Shi, Z. (2014). Image-Based 3D Semantic Modeling of Building Facade. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_79
Download citation
DOI: https://doi.org/10.1007/978-3-319-11331-9_79
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11330-2
Online ISBN: 978-3-319-11331-9
eBook Packages: Computer ScienceComputer Science (R0)