Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction

  • David Sedlacek
  • Jiri Zara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5876)


The reconstruction of 3D objects from a point-cloud is based on sufficient separation of the points representing objects of interest from the points of other, unwanted objects. This operation called segmentation is discussed in this paper. We present an interactive unstructured point-cloud segmentation based on graph cut method where the cost function is derived from euclidean distance of point-cloud points. The graph topology and direct 3D point-cloud segmentation are the novel parts of our work. The segmentation is presented on real application, the terrain reconstruction of a complex miniature paper model, the Langweil model of Prague.


segmentation graph cut point-cloud terrain reconstruction Langweil model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Joaquim, T.I.: In: Yuan, J.A.J., Xu, X., Nguyen, H., Shesh, A., Chen, B. (eds.) Eurographics workshop on sketch-based interfaces and modeling (2005)Google Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images, vol. 1, pp. 105–112 (2001)Google Scholar
  4. 4.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  5. 5.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1101–1113 (1993)CrossRefGoogle Scholar
  6. 6.
    Kahler, O., Rodner, E., Denzler, J.: On fusion of range and intensity information using graph cut for planar patch segmentation. Int. J. Intell. Syst. Technol. Appl. 5, 365–373 (2008)Google Scholar
  7. 7.
    Bleyer, M., Gelautz, M.: Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions. Image Commun 22, 127–143 (2007)Google Scholar
  8. 8.
    Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. In: SIGGRAPH 2003: ACM SIGGRAPH, pp. 954–961. ACM, New York (2003)CrossRefGoogle Scholar
  9. 9.
    Golovinskiy, A., Funkhouser, T.: Randomized cuts for 3d mesh analysis. In: SIGGRAPH Asia 2008: ACM SIGGRAPH, Asia, pp. 1–12. ACM, New York (2008)Google Scholar
  10. 10.
    Hornung, A., Kobbelt, L.: Robust reconstruction of watertight 3d models from non-uniformly sampled point clouds without normal information. In: SGP 2006: Proceedings of the fourth Eurographics symposium on Geometry processing, Aire-la-Ville, pp. 41–50. Eurographics Association, Switzerland (2006)Google Scholar
  11. 11.
    Sinha, S., Pollefeys, M.: Multi-view reconstruction using photo-consistency and exact silhouette constraints: a maximum-flow formulation. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005., vol. 1, pp. 349–356 (2005)Google Scholar
  12. 12.
    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. ICCV 2007, pp. 1–8 (2007)Google Scholar
  13. 13.
    Dorninger, P., Nothegger, C.: 3d segmentation of unstructured point clouds for building modelling, p. 191 (2007)Google Scholar
  14. 14.
    Rabbani, T., van den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint, pp. xx–yy (2006)Google Scholar
  15. 15.
    Jiang, X.Y., Bunke, H.: Fast segmentation of range images into planar regions by scan line grouping. Machine Vision and Applications, 115–122 (1994)Google Scholar
  16. 16.
    Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., Kang, S.B.: Image-based plant modeling. ACM Trans. Graph. 25(3), 599–604 (2006)CrossRefGoogle Scholar
  17. 17.
    Anguelov, D., Taskar, 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 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, pp. 169–176. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  18. 18.
    Sýkora, D., Dingliana, J., Collins, S.: Lazybrush: Flexible painting tool for hand-drawn cartoons. Comput. Graph. Forum 28(2), 599–608 (2009)CrossRefGoogle Scholar
  19. 19.
    Y., H.M.O., K., N.: Efficient and feature-preserving triangular mesh decimation. Journal of WSCG, 167–174 (2004)Google Scholar
  20. 20.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. Computer Graphics  21(1987)Google Scholar
  21. 21.
    Aurenhammer, F.: Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput. Surv. 23(3), 345–405 (1991)CrossRefGoogle Scholar
  22. 22.
    Amenta, N., Bern, M., Kamvysselis, M.: A new voronoi-based surface reconstruction algorithm. In: SIGGRAPH 1998: Proceedings of the 25th annual conference on Computer graphics and interactive techniques, pp. 415–421. ACM, New York (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Sedlacek
    • 1
  • Jiri Zara
    • 1
  1. 1.Faculty of Electrical EngineeringCzech Technical University in Prague 

Personalised recommendations