Skip to main content

Advertisement

Log in

Domain connected graph: the skeleton of a closed 3D shape for animation

  • original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In previous research, three main approaches have been employed to solve the skeleton extraction problem: medial axis transform (MAT), generalized potential field and decomposition-based methods. These three approaches have been formulated using three different concepts, namely surface variation, inside energy distribution, and the connectivity of parts. By combining the above mentioned concepts, this paper creates a concise structure to represent the control skeleton of an arbitrary object.

First, an algorithm is proposed to detect the end, connection and joint points of an arbitrary 3D object. These three points comprise the skeleton, and are the most important to consider when describing it. In order to maintain the stability of the point extraction algorithm, a prong-feature detection technique and a level iso-surfaces function-based on the repulsive force field was employed. A neighborhood relationship inherited from the surface able to describe the connection relationship of these positions was then defined. Based on this relationship, the skeleton was finally constructed and named domain connected graph (DCG). The DCG not only preserves the topology information of a 3D object, but is also less sensitive than MAT to the perturbation of shapes. Moreover, from the results of complicated 3D models, consisting of thousands of polygons, it is evident that the DCG conforms to human perception.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Amenta, N., Choi, S., Kolluri, R.: The power crust. In: Proc. SM 2001, pp. 249–260 (2001)

  2. Attali, D., Montanvert, A.: Modeling noise for a better simplification of skeletons. In: Proc. IEEICIP 1996 3, pp.13–16 (1996)

  3. Biasotti, S., Falcidieno, B., Spagnuolo, M.: Surface shape understanding based on extended reeb graphs. In: Rana, S., Wood,J. (eds.) Surface Topological Data Structures: An Introduction for Geographical Information Science, pp.87–103. Wiley, New York (2004)

  4. Bitter, I., Kaufman, A.E., Sato, M.: Penalized-distance volumetric skeleton algorithm. IEEE TVCG 7(3), pp. 195-206 (2001)

    Google Scholar 

  5. Blum, H.: A Transformation for Extracting New Descriptors of Shape. MIT Press, pp. 362–380 (1967)

  6. Bradshaw, G., O’Sullivan C.: Adaptive medial-axis approximation for sphere-tree construction. ACM TOG 23(1), pp. 1–26 (2004)

    Google Scholar 

  7. Borgefors, G., Nyström, I.: Efficient shape representation by minimizing the set of centres of maximal discs/spheres. Patt Recog Lett 18, 465–472 (1997)

    Google Scholar 

  8. Borgefors, G., Nyström, I., di Baja, G.S.: Computing skeletons in three dimensions. Patt Recog 32, 1225–1236 (1999)

    Google Scholar 

  9. Capell, S., Green, S., Curless, B., Duchamp, T., Popović, Z.: Interactive skeleton-driven dynamic deformations. ACM TOG 21(3), pp. 586–593 (Proc. SIGGRAPH 2002) (2002)

    Google Scholar 

  10. Choi, H.I., Choi, S.W., Moon, H.P.: Mathematical Theory of Medial Axis Transform. Pac J Math 181(1), 57–87 (1997)

    Google Scholar 

  11. Choi, S.W., Seidel, H.P.: One-sided stability of medial axis transform. Lecture Notes in Computer Science, Vol. 2191, pp. 132–139 (2001)

  12. Choi, W.-P., Lam., K.-M., Siu, W.-C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Patt Recog 36, 721–729 (2003)

    Google Scholar 

  13. Chung, J.-H., Tsai, C.-H., Ko, M.-C.: Skeletonization of three-dimensional object using generalized potential field. IEEE Trans Patt Anal Mach Intell 22(11), 1241–1251 (2000)

    Google Scholar 

  14. Culver, T., Keyser, J., Manocha, D.: Accurate computation of the medial axis of a polyhedron. Proceedings of ACM Symposium on Solid Modeling and Applications, pp. 179–190 (1999)

  15. Foskey, M., Lin, M.C., Manocha, D.: Efficient computation of a simplified medial axis. Proceedings of ACM Symposium on Solid Modeling and Applications, pp. 96–107 (2003)

  16. Giblin, P., Kimia, B. B.: A formal classification of 3d m.a. points and their local geometry. IEEE Trans Patt Anal Mach Intell 26(2), 238–251 (2004)

    Google Scholar 

  17. Grigorishin, T., Abdel-Hamid, G., Yang, Y.H.: Skeletonization: an electrostatic field-based approach. Patt Anal Appl 1, 163–177 (1998)

    Google Scholar 

  18. Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii T. L.: Topology matching for fully automatic similarity estimation of 3d shapes. SIGGRAPH 2001 Conference Proceedings, pp. 203–212 (2001)

  19. Hubbard, P.M.: Approximating polyhedra with spheres for time-critical collision detectio. ACM TOG 15(3), 179–210 (1996)

    Google Scholar 

  20. Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. SIGGRAPH 2003 Conference Proceedings, pp. 954–961 (2003)

  21. Kass, M., Witkin ,A., Terzopoulos, D.: Snakes: active contour models. International J Comput Vis 1, 321–331 (1987)

    Google Scholar 

  22. Kimia, B.B.: On the role of medial geometry in human vision. J Physiology-Paris 97(2-3), 155–190 (2003)

    Google Scholar 

  23. Kimia, F., Verroust, A.: Level set diagrams of polyhedral objects. In: ACM Symposium on Solid Modeling and Applications, pp. 130–140 (1999)

  24. Leymarie, F.F., Kimia, B., Giblin, B., Towards, P.J.: Surface regularization via medial axis transitions. International Conference on Pattern Recognition, pp. 123–126 (2004)

  25. Leymarie, F.F., Levine, M.D.: Simulating the grassfire transform using an active contour model. IEEE Trans Patt Anal Mach Intell 14(1), 56–75 (1992)

    Google Scholar 

  26. li, x.-t., woon, t.-w., tan, t.-s., huang, z.-y.: decomposing polygon meshes for interactive applications. Proceedings of ACM Symposium on Interactive 3D Graphics, pp. 35–42 (2001)

  27. Nilsson, F., Danielsson, P.-E.: Finding the minimal set of maximum disks for binary objects. Graph Model Im Proc 59(1), 55–60 (1997)

    Google Scholar 

  28. Ma, W.-C., Wu, F.-C., Ouhyoung M.: Skeleton extraction of 3d objects with radial basis function. Proceedings of Shape Modelling International 2003, pp. 207–215 (2003)

  29. Mayya, N., Rajan, V.T.: Voronoi diagrams of polygons: a framework for shape representation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 638–643 (1994)

  30. Mortara, M., Patane, G.: Affine-invariant skeleton of 3D shapes. Proceedings of Shape Modeling International 2002, pp. 245–278 (2002)

  31. Ogniewicz, R.: Automatic medial axis pruning by mapping characteristics of boundaries evolving under the Euclidean geometric heat flow onto voronoi skeletons. Technical Report 95-4, Harvard Robotics Laboratory (1995)

  32. Ogniewicz, R., Ilg, M.: Voronoi skeletons: theory and applications. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 63–69 (1992)

  33. Palenichka, R.M., Zaremba, M.B.: Multi-scale model-based skeletonization of object shapes using self-organizing maps. International Conference on Pattern Recognition 2002, pp. 10143–10147 (2002)

  34. Pizer, S.M., Thall, A.L., Chen, D.T. Chen: M-reps: a new object representation for graphics. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 638–643 (1994)

  35. Savchenko, V.V., Pasko, A.A., Okunev, O.G., Kunii T.L.: Function representation of solids reconstructed from scattered surface points and contours. Comput Graph Forum 14(4), 181–188 (1995)

    Google Scholar 

  36. Sheehy, D.J., Armstrong, C.G., Robinson, D.J.: Shape description by medial axis construction. IEEE Trans Visual Comput Graph 2(1), 62–72 (1996)

    Google Scholar 

  37. Sherbrooke, E.C., Sherbrooke, Patrikalakis, N.M., Brisson, E.: Computation of the medial axis transform of 3D polyhedra. Proceedings of ACM Symposium on Solid Modeling and Applications, pp. 187–199 (1995)

  38. Siddiqi, K., Bouix, S., Tannenbaum, A.R., Zucker, S.W.: Hamilton–Jacobi skeletons. Int J Comput Vis 48(3), 215–231 (2002)

    Google Scholar 

  39. Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. International Conference on Computer Vision, pp. 222–229 (1998)

  40. Shinagawa, Y., Kunii, T.L.: Constructing a Reeb graph automatically from cross sections. IEEE Comput Graph Appl 11(6), 44–51 (1991)

    Google Scholar 

  41. Tam, R., Heidrich, W.: Feature-preserving medial axis noise removal. ECCV2002, pp. 672–686 (2002)

  42. Verroust, A., Lazarus, F.: Extracting skeletal curves from 3d scattered data. Visual Comput 16(1), 15–25 (2000)

    Google Scholar 

  43. Wade, L., Parent, R.E.: Automated generation of control skeletons for use in animation. Visual Comput 18(2), 97–110 (2002)

    Google Scholar 

  44. Wolter, F.-E.: Cut locus and medial axis in global shape interrogation and representation. Technical Report, MIT (1993)

  45. Wu, F.C., Chen, B.Y., Liang, R.H., Ouhyoung, M.: Prong features detection of a 3d model based on the watershed algorithm. ACM SIGGRAPH2004 Sketches (2004)

  46. Wyvill, G., Handley, C.: The thermodynamics of shape. Proceedings of Shape Modeling International 2001, pp. 2–8 (2001)

  47. Zhou, Y., Toga, A.: Efficient skeletonization of volumetric objects. IEEE Trans Visual Comput Graph 5(3), 195–206 (1999)

    Google Scholar 

  48. Zhu, S.-C.: Stochastic jump-diffusion process for computing medial axes in markov random fields. IEEE Trans Pattern Anal Mach Intell 21(11), 1158–1169 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fu-Che Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, FC., Ma, WC., Liang, RH. et al. Domain connected graph: the skeleton of a closed 3D shape for animation. Visual Comput 22, 117–135 (2006). https://doi.org/10.1007/s00371-005-0357-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-005-0357-4

Keywords

Navigation