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Automatic Building Extraction from Terrestrial Laser Scanning DataHAO, W. , WANG, Y. , NING, X. , ZHAO, M. , ZHANG, J. , SHI, Z. , ZHANG, X.
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building extraction, point cloud segmentation, plane recognition, terrestrial laser scanning
data(13), sensing(9), remote(9), photogrammetry(8), segmentation(6), laser(6), extraction(6), ransac(5), point(5), isprs(5)
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About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 11 - 16
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03002
Web of Science Accession Number: 000326321600002
SCOPUS ID: 84884962911
The extraction of building from the huge amount of point clouds with different local densities, especially in the presence of random noisy points, is still a formidable challenge. In this paper, we present a complete strategy for building extraction from terrestrial laser scanning data. First, a novel segmentation method is proposed to facilitate the task of building extraction. The points are grouped based on the normals and the adjacency relationships. Second, the planar surfaces are recognized from the segmentation results based on the properties of the Gaussian image. Finally, the buildings are extracted from the urban point clouds based on a collection of characteristics of point cloud segments like shape, normal direction and topological relationship. Experimental results demonstrate that the proposed method can be used as a robust way to extract buildings from terrestrial laser scanning data. At the same time, the buildings are decomposed into several patches which lay a good foundation for building reconstruction.
|References|||||Cited By «-- Click to see who has cited this paper|
| N. Haala, C. Brenner, "Extraction of buildings and trees in urban environments," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 54, pp.130-137, 1999. |
[CrossRef] [Web of Science Times Cited 209] [SCOPUS Times Cited 284]
 M. Morgan, K. Tempfli, "Automatic building extraction from airborne laser scanning data," Archives of Photogrammetry and Remote Sensing, pp. 616-623, 2000.
 J. Dash, E. Steinle, R. P. Singh, H. P. Bahr, "Automatic building extraction from laser scanning data: an input tool for disaster management," Advances in space research, vol. 33, pp.317-322, 2004.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 39]
 J. Lam, K. Kusevic, P. Mrstik, R. Harrap and M. Greenspan, "Urban Scene Extraction from Mobile Ground Based LiDAR Data," International Symposium on 3D Data, Visualization & Transmission, pp.1-8, 2010.
 S. Barnea and S. Filin, "Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees," Remote Sensing, vol. 4, no. 1, pp.88-110, 2012.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]
 C. Bollesr, A. Fischler, "A ransac-based approach to model fitting and its application to finding cylinders in range data," In Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp.637-643, 1981.
 Hough, P.V.C., 1962. Method and means for recognizing complex patterns. U. S. Patent 3,069,654.
 F. Tarsha-Kurdi, T. Landes, P. Grussenmeyer, "Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from lidar data," ISPRS International Archives of Photogrammetry, Remote Sensing and Spatial Information Systems, vol.XXXVI, Part 3/W52, pp.407-412, 2007.
 T. Chaperon, F. Goulette. "Extracting cylinders in full 3D data using a random sampling method and the Gaussian image," In Proceedings of the Vision Modeling and Visualization Conference, pp.35-42, 2001.
 G. Roth, M.D. "Levine: Extracting geometric primitives," CVGIP: Image Understanding, vol. 58, no. 1, pp.1-22, 1993.
[CrossRef] [SCOPUS Times Cited 13]
 D. Nistere: "Preemptive RANSAC for live structure and motion estimation," Machine Vision and Applications, vol. 16, no. 5, 321-329, 2005.
[CrossRef] [Web of Science Times Cited 113] [SCOPUS Times Cited 144]
 R. Schnabel, R. Wahl, R. Klein, "Efficient RANSAC for point-cloud shape detection," Computer Graphics Forum, vol. 26, no. 2, pp.214-226, 2007.
[CrossRef] [Web of Science Times Cited 337] [SCOPUS Times Cited 468]
 F. Tarsha-Kurdi, T. Landes, P. Grussenmeyer, "Extended RANSAC algorithm for automatic detection of building roof planes from LIDAR data," The Photogrammetric Journal of Finland, vol. 21, no.1, pp.97-109, 2008.
 W. Yao, S. Hinz, U. Stilla, "Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis," ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, no.3, pp.260-271, 2011.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 26]
 T. Rabbani, Van den Heuvel F A., M. G.Vosselman, "Segmentation of point clouds using smoothness constraint," International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, no.5, pp.248-253, 2006.
 G. Vosselman, B. G. H. Gorte, G. Sithole, T. Rabbani, "Reconstruction of 3D building models from aerial images and maps," ISPRS Journal of Photogrammetry and Remote Sensing, vol.58, no.3, pp.202-224, 2004.
 J. Chen, B. Q. Chen, "Architectural modeling from sparsely scanned range data," International Journal of Computer Vision, vol.78, no.2-3, pp. 223-236, 2008.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 40]
 X. J. Ning, X. P. Zhang, Y.H. Wang, "Segmentation of architecture shape information from 3D point cloud," In Proc. of VRCAI 2009(8th Virtual Reality Continuum and its Applications in Industry), pp.127-132, 2009.
 S. Filin, "Surface clustering from airborne laser scanning data," International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.34, Part 3A, pp.117-124, 2002.
 G. Lavoue, F. Dupont, A. Baskurt, "A new CAD mesh segmentation method, based on curvature tensor analysis," Computer-Aided Design, vol. 37, no.10, pp.975-987, 2005.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 103]
 Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 17, no.8, pp.790-799, 1995.
[CrossRef] [Web of Science Times Cited 1256] [SCOPUS Times Cited 1946]
 D. Comaniciu, P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.24, no.5, pp.603-619, 2002.
[CrossRef] [Web of Science Times Cited 4008] [SCOPUS Times Cited 6162]
 Y. Liu, Y. L. Xiong, "Automatic segmentation of unorganized noisy point clouds based on the Gaussian map," Computer Aided Design, vol. 40, no.5, pp.576-594, 2008.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 40]
 J. M. Biosca, J. L. Lerma, "Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods," ISPRS Journal of Photogrammetry & Remote Sensing, vol.63, pp.84-98, 2008.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 67]
 K. Klasing., D.Wollherr, M. Buss, "A Clustering Method for Efficient Segmentation of 3D Laser Data," In International conference on Robotics and Automation, pp. 4043-4048, 2008.
 H. Hoppe, T. DeRose, T. Duchamp, "Surface reconstruction from unorganized points," In Proceedings of ACM SIGGRAPH, pp.71-78, 1992.
 T. S. Smith, R. T. Farouki, "Gauss map computation for free-form surfaces," Computer-Aided Geometric Design, vol.18, pp.831-850, 2001.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 17]
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