Skip to main content

Collision Detection Based on Fuzzy Scene Subdivision

  • Chapter
  • First Online:
GPU Computing and Applications

Abstract

We present a novel approach to perform collision detection queries between rigid and/or deformable models. Our method can handle arbitrary deformations and even discontinuous ones. For this, we subdivide the whole scene with all objects into connected but totally independent parts by a fuzzy clustering algorithm. Following, for every part, our algorithm performs a Principal Component Analyses to achieve the best sweep direction for the sweep-plane step, which reduces the number of false positives greatly. Our collision detection algorithm processes all computations without the need of a bounding volume hierarchy or any other acceleration data structure. One great advantage of this is that our method can handle the broad phase as well as the narrow phase within one single framework. Our collision detection algorithm works directly on all primitives of the whole scene, which results in a simpler implementation and can be integrated much more easily by other applications. We can compute inter-object and intra-object collisions of rigid and deformable objects consisting of many tens of thousands of triangles in a few milliseconds on a modern computer. We have evaluated its performance by common benchmarks.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://thrust.github.com

References

  1. Van den Bergen, G.: Efficient collision detection of complex deformable models using AABB trees. J. Graph. Tools 2(4), 1–13 (1997)

    Article  MATH  Google Scholar 

  2. Gottschalk S., Lin M. C., Manocha D.: OBBTree: A hierarchical structure for rapid interference detection. Comput. Graph. 30 (Annual Conference Series): 171–180 (1996)

    Google Scholar 

  3. Weller R., Zachmann G.: Inner sphere trees for proximity and penetration queries. In: Robotics: Science and Systems Conference (RSS), Seattle, WA, USA, June/July 2009

    Google Scholar 

  4. Gress A., Zachmann G.: Object-space interference detection on programmable graphics hardware. In: Lucian M. L., Neamtu M., (eds.) SIAM Conf. on Geometric Design and Computing, Seattle, Washington, 13–17 November 2003, pp. 311–328. Nashboro Press (2003)

    Google Scholar 

  5. Gress A., Guthe M., Klein R.: Gpu-based collision detection for deformable parameterized surfaces. Comput. Graph. Forum 25: 497–506 (2006)

    Google Scholar 

  6. Morvan T., Reimers M., Samset E.: High performance GPU-based proximity queries using distance fields. In: Computer graphics Forum, vol. 27, Wiley Online Library, pp. 2040–2052 (2008)

    Google Scholar 

  7. Govindaraju N., Knott D., Jain N., Kabul I., Tamstorf R., Gayle R., Lin M., Manocha D.: Interactive collision detection between deformable models using chromatic decomposition. ACM Trans. Graph. 24: 991–999 (2005)

    Google Scholar 

  8. Kim D., Heo J., Huh J., Kim J., Yoon S.: Hpccd: Hybrid parallel continuous collision detection using cpus and gpus. In: Computer Graphics Forum, vol. 28, Wiley Online Library, pp. 1791–1800 (2009)

    Google Scholar 

  9. Pabst S., Koch A., Strasser W.: Fast and Scalable CPU/GPU Collision Detection for Rigid and Deformable Surfaces. In: Computer Graphics Forum, vol. 29, Wiley Online Library, pp. 1605–1612 (2010)

    Google Scholar 

  10. Baraff D.: Dynamic simulation of non-penetrating rigid body simulation. PhD thesis, PhD thesis, Cornell University, (1992)

    Google Scholar 

  11. Cohen J., Lin M., Manocha D., Ponamgi M.: I-COLLIDE: An interactive and exact collision detection system for large-scale environments. In: Proceedings of the 1995 symposium on Interactive 3D graphics, ACM (1995)

    Google Scholar 

  12. Teschner M., Kimmerle S., Heidelberger B., Zachmann G., Raghupathi L., Fuhrmann A., P. Cani M., Faure F., Magnenat-Thalmann N., Strasser W., Volino P.: Collision detection for deformable objects. Comput. Graph. Forum, 61–81 (2004).

    Google Scholar 

  13. Ericson C.: Real-time collision detection. Morgan Kaufmann, San Francisco, CA (2004)

    Google Scholar 

  14. Lauterbach C., Mo Q., Manocha D.: gproximity: Hierarchical GPU-based operations for collision and distance queries. In Computer Graphics Forum (2010), vol. 29, Wiley Online Library, pp. 419–428

    Google Scholar 

  15. Tang M., Manocha D., Lin J., Tong R.: Collision-streams: fast gpu-based collision detection for deformable models. In: Symposium on Interactive 3D Graphics and Games, ACM, pp. 63–70 (2011)

    Google Scholar 

  16. Teschner M., Heidelberger B., Müller M., Pomeranets D., Gross M.: Optimized spatial hashing for collision detection of deformable objects. In: Proceedings of vision, modeling, visualization VMV’03, pp. 47–54 (2003)

    Google Scholar 

  17. Heidelberger B., Teschner M., GROSS M.: Real-time volumetric intersections of deforming objects. In: Proceedings of Vision, Modeling and Visualization, vol. 3, (2003)

    Google Scholar 

  18. Mainzer D., Zachmann G.: CDFC: Collision Detection Based on Fuzzy Clustering for Deformable Objects on GPU’s. In: WSCG 2013 - POSTER Proceedings, Plzen, Czech Republic, 7, vol. 21, pp. 5–8, Poster (2013)

    Google Scholar 

  19. Jolliffe I.: Principal component analysis. Wiley Online Library, (2005)

    Google Scholar 

  20. Liu F., Harada T., Lee Y., Kim Y. J.: Real-time collision culling of a million bodies on graphics processing units. In ACM Trans. Graph. 29: 154 (2010)

    Google Scholar 

  21. Bezdek J.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic, Norwell, MA (1981)

    Google Scholar 

  22. Pedrycz W.: Knowledge-based clustering: from data to information granules. Wiley-Interscience (2005)

    Google Scholar 

  23. Tang, M., Manocha, D., Tong, R.: MCCD: multi-core collision detection between deformable models using front-based. Decomposition. Graph. Models 72(2), 7–23 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

The cloth on ball and funnel simulation benchmarks are courtesy of the UNC Dynamic Scene Benchmarks collection and were provided by Naga Govindaraju, Ilknur Kabul, Stephane Redon, and Simon Pabst.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Mainzer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Mainzer, D., Zachmann, G. (2015). Collision Detection Based on Fuzzy Scene Subdivision. In: Cai, Y., See, S. (eds) GPU Computing and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-287-134-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-287-134-3_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-133-6

  • Online ISBN: 978-981-287-134-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics