The Visual Computer

, 26:63 | Cite as

Protrusion-oriented 3D mesh segmentation

  • Alexander AgathosEmail author
  • Ioannis Pratikakis
  • Stavros Perantonis
  • Nickolas S. Sapidis
Original Article


In this paper, we present a segmentation algorithm which partitions a mesh based on the premise that a 3D object consists of a core body and its constituent protrusible parts. Our approach is based on prominent feature extraction and core approximation and segments the mesh into perceptually meaningful components. Based upon the aforementioned premise, we present a methodology to compute the prominent features of the mesh, to approximate the core of the mesh and finally to trace the partitioning boundaries which will be further refined using a minimum cut algorithm. Although the proposed methodology is aligned with a general framework introduced by Lin et al. (IEEE Trans. Multimedia 9(1):46–57, 2007), new approaches have been introduced for the implementation of distinct stages of the framework leading to improved efficiency and robustness. The evaluation of the proposed algorithm is addressed in a consistent framework wherein a comparison with the state of the art is performed.


Mesh segmentation Prominent feature extraction Core approximation 


  1. 1.
    Agathos, A., Pratikakis, I., Perantonis, S., Sapidis, N., Azariadis, P.: 3D Mesh segmentation methodologies for CAD applications. Comput. Aided Des. Appl. 4(6), 827–841 (2007) Google Scholar
  2. 2.
    Attene, M., Katz, S., Mortara, M., Patane, G., Spagnuolo, M., Tal, A.: Mesh segmentation—a comparative study. In: IEEE International Conference on Shape Modeling and Applications. IEEE, Matsushima (2006) Google Scholar
  3. 3.
    Attene, M., Falcidieno, B., Spagnuolo, M.: Hierarchical segmentation based on fitting primitives. Vis. Comput. 22(3), 181–193 (2006) CrossRefGoogle Scholar
  4. 4.
    Hilaga, M., Shinagawa, Y., Komura, T., Kunii, T.L.: Topology matching for full automatic similarity estimation of 3D. In: SIGGRAPH, pp. 203–212. ACM, Los Angeles (2001) Google Scholar
  5. 5.
    Hoffman, D., Richards, W.: Parts of recognition. Cognition 18, 65–96 (1984) CrossRefGoogle Scholar
  6. 6.
    Karni, Z., Gotsman, C.: Spectral compression of mesh geometry. In: SIGGRAPH, pp. 279–286. ACM, New Orleans (2000) CrossRefGoogle Scholar
  7. 7.
    Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. 22(3), 954–961 (2003) CrossRefGoogle Scholar
  8. 8.
    Katz, S., Leifman, G., Tal, A.: Mesh segmentation using feature point and core extraction. Vis. Comput. 21(8–10), 639–648 (2005) Google Scholar
  9. 9.
    Kim, D.H., Yun, I.D., Lee, S.U.: A new shape decomposition scheme for graph-based representation. Pattern Recognit. 38(5), 673–689 (2005) CrossRefGoogle Scholar
  10. 10.
    Lee, Y., Lee, S., Shamir, A., Cohen-Or, D., Seidel, H.-P.: Mesh scissoring with minima rule and part salience. Comput. Aided Geom. Des. 22(5), 444–465 (2005) zbMATHCrossRefGoogle Scholar
  11. 11.
    Levy, B., Petitjean, S., Ray, N., Maillot, J.: Least squares conformal maps for automatic texture atlas generation. ACM Trans. Graph. 21(3), 362–371 (2002) CrossRefGoogle Scholar
  12. 12.
    Li, X., Toon, T., Tan, T., Huang, Z.: Decomposing polygon meshes for interactive applications. In: Proc. of the 2001 Symposium on Interactive 3D Graphics, pp. 35–42, NC, USA (2001) Google Scholar
  13. 13.
    Lin, H.S., Liao, H.M., Lin, J.: Visual salience-guided mesh decomposition. IEEE Trans. Multimedia 9(1), 46–57 (2007) CrossRefGoogle Scholar
  14. 14.
    Page, D., Koschan, A., Abidi, M.: Perception-based 3D triangle mesh segmentation using fast marching watersheds. In: Proc. Intl. Conf. on Computer Vision and Pattern Recognition, pp. 27–32, Wisconsin, USA (2003) Google Scholar
  15. 15.
    Shamir, A.: Segmentation and shape extraction of 3D boundary meshes. In: State-of-the-Art Report, Proceedings Eurographics (2006) Google Scholar
  16. 16.
    Shapira, L., Shamir, A., Cohen-Or, Lior Shapira, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis. Comput. 24(4), 249–259 (2008) CrossRefGoogle Scholar
  17. 17.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000) CrossRefGoogle Scholar
  18. 18.
    Shlafman, S., Tal, A., Katz, S.: Metamorphosis of polyhedral surfaces using decomposition. In: Eurographics, pp. 219–228. Eurographics Association, Saarbrücken (2002) Google Scholar
  19. 19.
    Sukumar, S.R., Page, D.L., Koschan, A.F., Gribok, A.V., Abidi, M.A.: Shape measure for identifying perceptually informative parts of 3D objects. In: 3D Data Processing, Visualization and Transmission, pp. 679–686. IEEE, Chapel Hill (2006) CrossRefGoogle Scholar
  20. 20.
    Valette, S., Kompatsiaris, I., Strintzis, M.G.: A polygonal mesh partitioning algorithm based on protrusion conquest for perceptual 3D shape description. In: Workshop towards Semantic Virtual Environments, pp. 68–76. Villars, Switzerland (2005) Google Scholar
  21. 21.
    Wu, K., Levine, M.D.: 3D Part Segmentation Using Simulated Electrical Charge Distributions. Trans. Pattern Anal. Mach. Intell. 19(11), 1223–1235 (1997) CrossRefGoogle Scholar
  22. 22.
    Zhang, H., Liu, R.: Mesh segmentation via recursive and visually salient spectral cuts. In: Vision, Modeling, and Visualization, pp. 429–436. Erlangen (2005) Google Scholar
  23. 23.
    Zhang, Y., Paik, J., Koschan, A., Abidi, M.A.: A simple and efficient algorithm for part decomposition of 3D triangulated models based on curvature analysis. In: International Conference on Image Processing, pp. 273–276. IEEE, Rochester (2002) Google Scholar
  24. 24.
    Zuckerberger, E., Tal, A., Shlafman, S.: Polyhedral surface decomposition with applications. Comput. Graph. 5, 733–743 (2002) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Alexander Agathos
    • 1
    • 2
    Email author
  • Ioannis Pratikakis
    • 1
  • Stavros Perantonis
    • 1
  • Nickolas S. Sapidis
    • 2
  1. 1.Computational Intelligence Laboratory, Institute of Informatics and TelecommunicationsNCSR ‘Demokritos’AthensGreece
  2. 2.Department of Product and Systems Design EngineeringUniversity of the AegeanMytileneGreece

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