Skip to main content
SpringerLink
Log in

Continuous collision detection for deformable objects using permissible clusters

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In this paper, we propose a new data structure to perform continuous collision detection (CCD) for deformable triangular meshes. The critical component of this data structure is permissible clusters. At the preprocessing phase, the triangular meshes are divided into permissible clusters. Then, the features of the triangular meshes are assigned to the permissible clusters. At the runtime phase, the potentially colliding feature pairs are collected and they are processed only once in the elementary processing. Our method has been integrated with a normal cone-based method and compared with other CCD methods. Experimental results show that our method improves the overall performance of CCD for deformable objects.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bridson, R., Fedkiw, R., Anderson, J.: Robust treatment of collisions, contact and friction for cloth animation. ACM Trans. Gr. 21(3), 594–603 (2002)

    Google Scholar 

  2. Curtis, S., Tamstorf, R., Manocha, D.: Fast collision detection for deformable models using representative-triangles. Proc. Symp. I3D, 61–69 (2008)

    Google Scholar 

  3. Gottschalk, S., Lin, M., Manocha, D.: OBBTree: A Hierarchical Structure for Rapid Interference Detection, pp. 171–180. In: Computer Graphics Proceedings. ACM SIGGRAPH (1996)

  4. UNC-SelfCCD: Self-CCD: continuous collision detection for deforming objects. http://gamma.cs.unc.edu/SELFCD/. Last visit on 9 Apr 2012

  5. Hutter M., Fuhrmann A.: Optimized continuous collision detection for deformable triangle meshes. In: Proceedings of WSCG, pp. 35–32 (2007)

  6. Hubbard, P.: Interactive collision detection, pp. 24–31. Virtual Reality. In: Proceedings of Symp. on research frontiers (1993)

  7. Heo J.-P., Seong J.-K., Kim D., Otaduy M.-A., Hong J.-M., Tang M, Yoon S.-E.: FASTCD: fracturing-aware stable collision detection. In: Proceedings of ACM SIGGRAPH/Eurographics Symp. on computer, animation (2010)

  8. Kim, D., Heo, J., Huh, J., Kim, J., Yoon, S.: Hpccd: hybrid parallel continuous collision detection using cpus and gpus. Computer Gr. Forum 28(7), 1791–1800 (2009)

    Article  Google Scholar 

  9. Klosowski, J., Held, M., Mitchell, J., Sowizral, H., Zikan, K.: Efficient collision detection using bounding volume hierarchies of k-dops. IEEE Trans. Vis. Computer Gr. 4(1), 21–36 (1998)

    Google Scholar 

  10. Larsson, T., Akenine-Möller, T.: A dynamic bounding volume hierarchy for generalized collision detection. Computers Gr. 30(3), 450–459 (2006)

    Article  Google Scholar 

  11. Liu, J., Ko, M., Chang, R.: Collision avoidance in cloth animation. Vis. Computer 12(5), 234–243 (1996)

    Article  MATH  Google Scholar 

  12. Madera F., Day A., Laycock S.: A hybrid bounding volume algorithm to detect collisions between deformable objects. In: Int’l conference on advances in computer-human interactions, pp. 136–141 (2009)

  13. Mezger, J., Kimmerle, S., Etzmuss, O.: Hierarchical techniques in collision detection for cloth animation. J. WSCG 11(2), 322–329 (2003)

    Google Scholar 

  14. Madera, F., Laycock, S., Day, A.: Detecting self-collisions using a hybrid bounding, vol. algorithm. Interactions, pp. 107–112. In: Int’l Conf. on advances in computer-human (2010)

  15. Provot X.: Collision and self-collision handling in cloth model dedicated to design garments. In: Graphics interface, pp. 177–189 (1997)

  16. Smith A., Kitamura Y., Takemura H., Kishino F.: A simple and efficient method for accurate collision detection among deformable polyhedral objects in arbitrary motion. In: Proceedings of the virtual reality annual International Symposium, pp. 136–145 (1995)

  17. Tang M., Curtis S., Yoon S., Manocha D.: Interactive continuous collision detection between deformable models using connectivity-based culling. In: ACM solid and physical modeling symposium, pp. 25–36 (2008)

  18. Tang, M., Curtis, S., Yoon, S., Manocha, D.: ICCD: interactive continuous collision detection between deformable models using connectivity-based culling. IEEE Trans. Vis. Computer Gr. 15(4), 544–557 (2009)

    Article  Google Scholar 

  19. Tang C., Li S., Wang G.: Fast continuous collision detection using parallel filter in subspace. In: ACM SIGGRAPH symposium on interactive 3D graphics and games (i3D), pp. 71–80 (2011)

  20. Tang M., Manocha D., Non-penetration filters, source code used in fast continuous collision detection using deforming non-penetration filters. http://gamma.cs.unc.edu/DNF/DNF-code.cpp (2010)

  21. Tang M., Manocha D., Tong R.: Multi-core collision detection between deformable models. In: SIAM/ACM joint conference on geometric and physical modeling, pp. 355–360 (2009)

  22. Tang M., Manocha D., Tong R.: Fast continuous collision detection using deforming non-penetration filters. In: ACM SIGGRAPH symp. interactive 3D graphics and games, pp. 7–13 (2010)

  23. van den Bergen, G.: Efficient collision detection of complex deformable models using aabb trees. J. Gr. GPU Game Tools 2(4), 1–14 (1999)

    Google Scholar 

  24. Volino P., Magnenat-Thalmann N.: Efficient self-collision detection on smoothly discretized surface animations using geometrical shape regularity. In: Computer graphics forum, pp. 155–166 (1994)

  25. Wong, S.-K., Baciu, G.: Dynamic interaction between deformable surfaces and non-smooth objects. IEEE Trans. Vis. Computer Gr. 11(3), 329–340 (2005)

    Article  Google Scholar 

  26. Wong S., Baciu G.: A randomized marking scheme for continuous collision detection in simulation of deformable surfaces. In: Proceedings of ACM Int’l Conf. on virtual reality continuum and its applications, pp. 181–188 (2006)

  27. Wong S.-K., Baciu G., Liu C.-M., Yeh C.-C.: Robust continuous collision detection for deformable objects. In: Proceedings of ACM symposium on virtual reality software and technology (2010)

  28. Wong, S.-K., Lin, W.-C., Hung, C.-H., Huang, Y.-J., Lii, S.-Y.: Radial view based culling for continuous self-collision detection of skeletal models. ACM Trans. Gr. 32, 4 (2013)

    Article  Google Scholar 

  29. Zachmann G., Clausthal T.: Kinetic bounding volume hierarchies for deformable objects. In: Proceedings of ACM Int’l Conf. on virtual reality continuum and its application, pp. 14–17 (2006)

  30. Zheng, C., Doug, J.: Energy-based self-collision culling for arbitrary mesh deformations. ACM Trans. Gr. 31, 4 (2012)

    Google Scholar 

Download references

Acknowledgments

We thank the reviewers for their constructive and invaluable comments. The animation data of Cloth and Balls were obtained from the UNC Gamma Group. This work was supported in part by the National Science Council Taiwan under contract number NSC 102-2221-E-009-103-MY2 and Hong Kong RGC GRF grants (PolyU 5101/11E, PolyU 5100/12E and PolyU 5100/13E).

Author information

Authors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan, ROC

    Sai-Keung Wong

  2. Hong Kong Polytechnic University, Kowloon, Hong Kong

    George Baciu

Authors
  1. Sai-Keung Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Baciu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sai-Keung Wong.

Appendix A: Properties of the permissible clusters

Appendix A: Properties of the permissible clusters

We give a proof for the properties of the permissible clusters (see Sect. 4.1). Assume that the clusters are free of collision before the simulation time interval.

  1. 1.

    Type I: a single triangle is free of collision on its own. This is because the edges of the triangles are connected to each other and the vertices of the triangle are adjacent to the triangle.

  2. 2.

    Type II: if two non-adjacent features of the cluster collide with each other, then the two triangles of the cluster must be coplanar and their normal vectors point to the opposite directions. The cluster does not have a valid continuous normal cone.

  3. 3.

    Type III: a Type III cluster is a 2-manifold mesh with boundary. The triangles of the cluster share a common vertex at the center and each triangle is adjacent to another two triangles. If the cluster collides with itself, there must be an external vertex colliding with the supporting plane of a triangle of the cluster. In this case, there are two or more than two disjointed subspaces which are formed by the half spaces of the supporting planes of the triangles. The cluster does not have a valid continuous normal cone. One of the subspace may be empty. An example is illustrated in Fig. 8.

  4. 4.

    Type IV: it is unnecessary to check a Type IV cluster as all the internal edges of the cluster are connected at the common vertex.

Fig. 8
figure 8

A Type III cluster. It collides with itself when it deforms in three cases. Its contour is drawn on the \(xy\)-plane for the three cases. a The initial shape of the cluster; b a vertex \(p_2\) collides with a triangle (\(p_0\), \(p_8\), \(p_7\)); c an edge \(p_0p_7\) collides with an edge \(p_2p_3\); d a triangle (\(p_0\), \(p_3\), \(p_2\)) with another triangle (\(p_0\), \(p_7\), \(p_6\)). There are three colliding edge–edge pairs

Full size image

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wong, SK., Baciu, G. Continuous collision detection for deformable objects using permissible clusters. Vis Comput 31, 377–389 (2015). https://doi.org/10.1007/s00371-014-0933-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-014-0933-6

Keywords

Search

Navigation