The Visual Computer

, Volume 28, Issue 6–8, pp 775–785 | Cite as

Feature-varying skeletonization

Intuitive control over the target feature size and output skeleton topology
  • Chris G. WillcocksEmail author
  • Frederick W. B. Li
Original Article


Current skeletonization algorithms strive to produce a single centered result which is homotopic and insensitive to surface noise. However, this traditional approach may not well capture the main parts of complex models, and may even produce poor results for applications such as animation. Instead, we approximate model topology through a target feature size ω, where undesired features smaller than ω are smoothed, and features larger than ω are retained into groups called bones. This relaxed feature-varying strategy allows applications to generate robust and meaningful results without requiring additional parameter tuning, even for damaged, noisy, complex, or high genus models.


Automatic Skeletonization Contraction Feature abstraction Surface noise Topology 



The work described in this paper was supported by a UK EPSRC grant (Project Number: EP/G009635/1). Special thanks go to Anna Willcocks for all her love and support.


  1. 1.
    Arcelli, C., di Baja, G., Serino, L.: Distance-driven skeletonization in voxel images. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 709–720 (2011). doi: 10.1109/TPAMI.2010.140 CrossRefGoogle Scholar
  2. 2.
    Au, O.K.C., Tai, C., Chu, H., Cohen-Or, D., Lee, T.: Skeleton extraction by mesh contraction. In: SIGGRAPH 2008 Papers, pp. 44:1–44:10. ACM, New York (2008). doi: 10.1145/1399504.1360643 Google Scholar
  3. 3.
    Aujay, G., Hétroy, F., Lazarus, F., Depraz, C.: Harmonic skeleton for realistic character animation. In: SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’07, pp. 151–160. Eurographics Association, Aire-la-Ville (2007). doi: 10.2312/SCA/SCA07/151-160 Google Scholar
  4. 4.
    Baran, I., Popovic, J.: Automatic rigging and animation of 3D characters. In: SIGGRAPH 2007 Papers, p. 72. ACM, New York (2007). doi: 10.1145/1275808.1276467 CrossRefGoogle Scholar
  5. 5.
    Biasotti, S., Attali, D., Boissonnat, J., Edelsbrunner, H., Elber, G., Mortara, M., Baja, G., Spagnuolo, M., Tanase, M., Veltkamp, R.: Skeletal structures. In: Shape Analysis and Structuring, Mathematics and Visualization, pp. 145–183. Springer, Berlin (2008). doi: 10.1007/978-3-540-33265-7_5 CrossRefGoogle Scholar
  6. 6.
    Biasotti, S., Giorgi, D., Spagnuolo, M., Falcidieno, B.: Reeb graphs for shape analysis and applications. Theor. Comput. Sci. 392(1–3), 5–22 (2008). doi: 10.1016/j.tcs.2007.10.018 MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Boissonnat, J., Oudot, S.: Provably good sampling and meshing of surfaces. Graph. Models 67(5), 405–451 (2005). doi: 10.1016/j.gmod.2005.01.004 zbMATHCrossRefGoogle Scholar
  8. 8.
    Brunner, D., Brunnett, G.: An extended concept of voxel neighborhoods for correct thinning in mesh segmentation. In: SCCG, Budmerice, Slovakia, p. 119 (2005). doi: 10.1145/1090122.1090143 CrossRefGoogle Scholar
  9. 9.
    Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., Su, Z.: Point cloud skeletons via laplacian based contraction. In: SMI, pp. 187–197. IEEE Press, Washington (2010). doi: 10.1109/SMI.2010.25 Google Scholar
  10. 10.
    Cornea, N., Silver, D., Yuan, X., Balasubramanian, R.: Computing hierarchical curve-skeletons of 3D objects. Vis. Comput. 21(11), 945–955 (2005). doi: 10.1007/s00371-005-0308-0 CrossRefGoogle Scholar
  11. 11.
    Cornea, N., Silver, D., Min, P.: Curve-Skeleton properties, applications, and algorithms. IEEE Trans. Vis. Comput. Graph. 13(3), 530–548 (2007). doi: 10.1109/TVCG.2007.1002 CrossRefGoogle Scholar
  12. 12.
    Desbrun, M., Meyer, M., Schröder, P., Barr, A.: Implicit fairing of irregular meshes using diffusion and curvature flow. In: Computer Graphics and Interactive Techniques, SIGGRAPH, pp. 317–324. ACM/Addison-Wesley, New York (1999). doi: 10.1145/311535.311576 CrossRefGoogle Scholar
  13. 13.
    Dey, T., Sun, J.: Defining and computing curve-skeletons with medial geodesic function. In: Geometry Processing, Eurographics, SGP, pp. 143–152. Eurographics Association, Aire-la-Ville (2006) Google Scholar
  14. 14.
    Hasler, N., Thormahlen, T., Rosenhahn, B., Seidel, H.: Learning skeletons for shape and pose. In: Interactive 3D Graphics and Games—I3D SIGGRAPH, Washington, USA, p. 23 (2010). doi: 10.1145/1730804.1730809 Google Scholar
  15. 15.
    Hassouna, M., Farag, A.: Variational curve skeletons using gradient vector flow. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2257–2274 (2009). doi: 10.1109/TPAMI.2008.271 CrossRefGoogle Scholar
  16. 16.
    He, Y., Xiao, X., Seah, H.: Harmonic 1-form based skeleton extraction from examples. Graph. Models 71(2), 49–62 (2009). doi: 10.1016/j.gmod.2008.12.008 CrossRefGoogle Scholar
  17. 17.
    Ho, C., Wu, F., Chen, B., Chuang, Y., Ouhyoung, M.: Cubical marching squares: adaptive feature preserving surface extraction from volume data. Eurographics 24(3), 537–545 (2005). doi: 10.1111/j.1467-8659.2005.00879.x Google Scholar
  18. 18.
    Li, F.W.B., Lau, R.W.H., Kilis, D., Li, L.W.F.: Game-on-demand: an online game engine based on geometry streaming. ACM Trans. Multimed. Comput. Commun. Appl. 7(3), 19:1–19:22 (2011). doi: 10.1145/2000486.2000493 Google Scholar
  19. 19.
    Lindholm, E., Kligard, M., Moreton, H.: A user-programmable vertex engine. In: SIGGRAPH, pp. 149–158 (2001). doi: 10.1145/383259.383274 Google Scholar
  20. 20.
    Liu, L., Chambers, E., Letscher, D., Ju, T.: A simple and robust thinning algorithm on cell complexes. Comput. Graph. Forum 29(7), 2253–2260 (2010). doi: 10.1111/j.1467-8659.2010.01814.x CrossRefGoogle Scholar
  21. 21.
    Marinov, M., Kobbelt, L.: Automatic generation of structure preserving multiresolution models. Eurographics 24(3), 479–486 (2005). doi: 10.1111/j.1467-8659.2005.00873.x Google Scholar
  22. 22.
    Miklos, B., Giesen, J., Pauly, M.: Discrete scale axis representations for 3D geometry. In: SIGGRAPH, pp. 101:1–101:10. ACM, New York (2010). doi: 10.1145/1833349.1778838 Google Scholar
  23. 23.
    Mount, D., Arya, S.: ANN: A library for approximate nearest neighbor searching (2010). Accessed 25 January 2012
  24. 24.
    Ning, X., Li, E., Zhang, X., Wang, Y.: Shape decomposition and understanding of point cloud objects based on perceptual information. In: VRCAI, SIGGRAPH, Seoul, South Korea, p. 199 (2010). doi: 10.1145/1900179.1900221 Google Scholar
  25. 25.
    Oda, T., Itoh, Y., Nakai, W., Nomura, K., Kitamura, Y., Kishino, F.: Interactive skeleton extraction using geodesic distance. In: ICAT, Hangzhou, Zhejiang, China, pp. 275–281 (2006). doi: 10.1109/ICAT.2006.76 Google Scholar
  26. 26.
    Pantuwong, N., Sugimoto, M.: Skeleton-growing: a vector-field-based 3D curve-skeleton extraction algorithm. In: SA, pp. 6:1–6:2. ACM, New York (2010). doi: 10.1145/1899950.1899956 Google Scholar
  27. 27.
    Tagliasacchi, A., Zhang, H., Cohen-Or, D.: Curve skeleton extraction from incomplete point cloud. In: SIGGRAPH, pp. 71:1–71:9. ACM, New York (2009). doi: 10.1145/1576246.1531377 Google Scholar
  28. 28.
    Wang, Y., Lee, T.: Curve-Skeleton extraction using iterative least squares optimization. IEEE Trans. Vis. Comput. Graph. 14, 926–936 (2008). doi: 10.1109/TVCG.2008.38 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of Engineering and Computing SciencesDurham UniversityDurhamEngland

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