A Novel Hierarchical Technique for Range Segmentation of Large Building Exteriors

  • Reyhaneh Hesami
  • Alireza Bab-Hadiashar
  • Reza Hosseinnezhad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


Complex multiple structures, high uncertainty due to the existence of moving objects, and significant disparity in the size of features are the main issues associated with processing range data of outdoor scenes. The existing range segmentation techniques have been commonly developed for laboratory sized objects or simple architectural building features. In this paper, main problems related to the geometrical segmentation of large and significant buildings are studied. A robust and accurate range segmentation approach is also devised to extract very fine geometric details of building exteriors. It uses a hierarchical model-base range segmentation strategy and employs a high breakdown point robust estimator to deal with the existing discrepancies in size and sampling rates of various features of large outdoor objects. The proposed range segmentation algorithm facilitates automatic generation of fine 3D models of environment. The computational advantages and segmentation capabilities of the proposed method are shown using real range data of large building exteriors.


Segmentation Algorithm Range Data Range Image Robust Estimator Outdoor Scene 
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.


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  1. 1.
    Stamos, I., Allen, P.: Geometry and Texture Recovery of Scenes of Large Scale. Computer Vision and Image Understanding (CVIU) 88(2), 94–118 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Cantzler, H., Fisher, R.B., Devy, M.: Improving Architectural 3D Reconstruction by Plane and Edge Constraining. In: (BMVC), Britain, pp. 43–52 (2002)Google Scholar
  3. 3.
    Zhao, H., Shibasaki, R.: A Vehicle-Borne Urban 3D Acquisition System using Single-row Laser Range Scanners. Systems, Man and Cybernetics, Part B 33(4), 658–666 (2003)CrossRefGoogle Scholar
  4. 4.
    Anguelov, D., Taskarf, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.: Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. In: CVPR, CA, USA, pp. 169–176 (2005)Google Scholar
  5. 5.
    Triebel, R., Kersting, K., Burgard, W.: Robust 3D Scan Point Classification Using Associative Markov Networks. In: (ICRA), Florida, USA, pp. 2603–2608 (2006)Google Scholar
  6. 6.
    Lakaemper, R., Latecki, L.J.: Using Extended EM to Segment Planar Structures in 3D. In: (ICPR), Hong Kong, pp. 1077–1082 (2006)Google Scholar
  7. 7.
    Bab-Hadiashar, A., Suter, D.: Robust Segmentation of Visual Data Using Ranked Unbiased Scale Estimate. ROBOTICA 17, 649–660 (1999)CrossRefGoogle Scholar
  8. 8.
    Bab-Hadiashar, A., Gheissari, N.: Range Image Segmentation Using Surface Selection Criterion. IEEE Trans. on Image Processing 15(7), 2006–2018 (2006)CrossRefGoogle Scholar
  9. 9.
    Stamos, I., Yu, G., Wolberg, G., Zokai, S.: 3D Modelling Using Planar Segments and Mesh Elements. In: (3DPVT), University of North Carolina, pp. 599–606. Chapel Hill (2006)Google Scholar
  10. 10.
    Allen, P.K., Stamos, I., Troccoli, A., Smith, B., Leordeanu, M., Hsu, Y.C.: 3D Modeling of Historic Sites Using Range and Image Data. In: (ICRA), Taiwan, pp. 145–150 (2003)Google Scholar
  11. 11.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–393 (1981)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Meer, P.: Robust Techniques for Computer Vision. In: Emerging Topics in Computer Vision, pp. 107–190. Prentice Hall, Englewood Cliffs (2004)Google Scholar
  13. 13.
    Huber, P.J.: Robust Statistics. Wiley, New York (1981)zbMATHGoogle Scholar
  14. 14.
    Rousseeuw, P.J.: Least Median of Squares Regression. Journal of American Statistical Association 79, 871–880 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Chen, H., Meer, P.: Robust Regression with Projection Based M-Estimators. In: (ICCV), Nice, France, pp. 878–885 (2003)Google Scholar
  16. 16.
    Hoseinnezhad, R., Bab-Hadiashar, A.: A Novel High Breakdown M-estimator for Visual Data Segmentation. In: (ICCV), Rio de Janeiro, Brazil (October 2007)Google Scholar
  17. 17.
    Hoseinnezhad, R., Bab-Hadiashar, A., Suter, D.: Finite Sample Bias of Robust Scale Estimators in Computer Vision Problems. In: (ISVC), Nevada, USA, pp. 445–454 (November 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Reyhaneh Hesami
    • 1
  • Alireza Bab-Hadiashar
    • 1
  • Reza Hosseinnezhad
    • 1
  1. 1.Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, VIC 3127Australia

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