Skip to main content
SpringerLink
Log in

Tracking Data Structures Coherency in Animated Ray Tracing: Kalman and Wiener Filters Approach

  • Conference paper
Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5358))

Included in the following conference series:

  • 2378 Accesses

Abstract

The generation of natural and photorealistic images in computer graphics, normally make use of a well known method called ray tracing. Ray tracing is being adopted as a primary image rendering method in the research community for the last few years. With the advent of todays high speed processors, the method has received much attention over the last decade. Modern power of GPUs/CPUs and the accelerated data structures are behind the success of ray tracing algorithms. kd-tree is one of the most widely used data structures based on surface area heuristics (SAH). The major bottleneck in kd-tree construction is the time consumed to find optimum split locations. In this paper, we propose a prediction algorithm for animated ray tracing based on Kalman and Wiener filters. Both the algorithms successfully predict the split locations for the next consecutive frame in the animation sequence. Thus, giving good initial starting points for one dimensional search algorithms to find optimum split locations – in our case parabolic interpolation combined with golden section search. With our technique implemented, we have reduced the “running kd-tree construction” time by between 78% and 87% for dynamic scenes with 16.8K and 252K polygons respectively.

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

Access this chapter

Log in via an institution
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 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Havran, V.: Heuristic Ray Shooting Algorithms. PhD thesis, Faculty of Electrical Engineering, Czech Technical University in Prague (2001)

    Google Scholar 

  2. MacDonald, J.D., Booth, K.S.: Heuristics for Ray Tracing Using Space Subdivision. In: Graphics Interface Proceedings 1989, Wellesley, MA, USA, June 1989, pp. 152–163. A.K. Peters, Ltd. (1989)

    Google Scholar 

  3. Stoll, G.: Part I: Introduction to Realtime Ray Tracing. In: SIGGRAPH 2005 Course on Interactive Ray Tracing (2005)

    Google Scholar 

  4. Zara, J.: Speeding Up Ray Tracing - SW and HW Approaches. In: Proceedings of 11th Spring Conference on Computer Graphics (SSCG 1995), Bratislava, Slovakia, pp. 1–16 (May 1995)

    Google Scholar 

  5. Hunt, W., Stoll, G., Mark, W.: Fast kd-tree Construction With An Adaptive Error-Bounded Heuristic. In: Proceedings of the 2006 IEEE Symposium on Interactive Ray Tracing, pp. 81–88 (September 2006)

    Google Scholar 

  6. Wald, I., Havran, V.: On Building Fast kd-trees For Ray Tracing, and on Doing That In O(N log N). In: Proceedings of the 2006 IEEE Symposium on Interactive Ray Tracing, pp. 61–69 (September 2006)

    Google Scholar 

  7. Woop, S., Marmitt, G., Slusallek, P.: B-kd trees for Hardware Accelerated Ray Tracing of Dynamic Scenes. In: Proceedings of Graphics Hardware (2006)

    Google Scholar 

  8. Foley, T., Sugerman, J.: kd-tree Acceleration Structures For A GPU Raytracer. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, pp. 15–22 (2005)

    Google Scholar 

  9. Hussain, S., Grahn, H.: Fast kd-Tree Construction for 3D-Rendering Algorithms like Ray Tracing. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part II. LNCS, vol. 4842, pp. 681–690. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Wald, I.: Realtime Ray Tracing and Interactive Global Illumination. PhD thesis, Computer Graphics Group, Saarland University, Saarbrucken, Germany (2004)

    Google Scholar 

  11. Havran, V.: Heuristic Ray Shooting Algorithm. PhD thesis, Czech Technical University, Prague (2001)

    Google Scholar 

  12. Chang, A.Y.: Theoretical and Experimental Aspects of Ray Shooting. PhD Thesis, Polytechnic University, New York (May 2004)

    Google Scholar 

  13. Havran, V., Herzog, R., Seidel, H.-P.: On Fast Construction of Spatial Hierarchies for Ray Tracing. In: Proceedings of the 2006 IEEE Symposium on Interactive Ray Tracing, pp. 71–80 (September 2006)

    Google Scholar 

  14. Benthin, C.: Realtime Raytracing on Current CPU Architectures. PhD thesis, Saarland University (2006)

    Google Scholar 

  15. Popov, S., Gunther, J., Seidel, H.-P., Slusallek, P.: Experiences with Streaming Construction of SAH KD-Trees. In: Proceedings of IEEE Symposium on Interactive Ray Tracing, pp. 89–94 (September 2006)

    Google Scholar 

  16. Cleary, J.G., Wyvill, G.: Analysis Of An Algorithm For Fast Ray Tracing Using Uniform Space Subdivision. The Visual Computer (4), 65–83 (1988)

    Article  Google Scholar 

  17. Whang, K.-Y., Song, J.-W., Chang, J.-W., Kim, J.-Y., Cho, W.-S., Park, C.-M., Song, I.-Y.: An Adaptive Octree for Effi¬cient Ray Tracing. IEEE Transactions on Visualization and Computer Graphics 1(4), 343–349 (1995)

    Article  Google Scholar 

  18. Horn, D.R., Sugerman, J., Houston, M., Hanrahan, P.: Interactive kd-tree GPU Raytracing. In: Symposium on Interactive 3D Graphics. I3D, pp. 167–174 (2007)

    Google Scholar 

  19. Redmonds, S.J., Heneghan, C.: A Method for Initializing the K-Means Clustering Algorithm Using kd-trees. Pattern Recognition Letters 28(8), 965–973 (2007)

    Article  Google Scholar 

  20. Stern, H.: Nearest Neighbor Matching Using kd-Trees. PhD thesis, Dalhousie University, Halifax, Nova Scotia (August 2002)

    Google Scholar 

  21. Kaplan, M.: The Use of Spatial Coherence in Ray Tracing. In: ACM SIGGRAPH 1985 Course Notes, vol. 11, pp. 22–26 (July 1985)

    Google Scholar 

  22. Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME—Journal of Basic Engineering, 35–45 (March 1960)

    Google Scholar 

  23. Welch, G., Bishop, G.: An Introduction to Kalman Filter. Department of Computer Science, University of North Carolina (July 2006)

    Google Scholar 

  24. Grewal, M.S., Andrews, A.P.: Kalman Filtering, Theory and Practice. Prentice Hall, Englewood Cliffs (1993)

    MATH  Google Scholar 

  25. Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York (1949)

    Book  MATH  Google Scholar 

  26. Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice Hall, New Jersey (1996)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Blekinge Institute of Technology, SE-371 79, Karlskrona, Sweden

    Sajid Hussain & Håkan Grahn

Authors
  1. Sajid Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Håkan Grahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hussain, S., Grahn, H. (2008). Tracking Data Structures Coherency in Animated Ray Tracing: Kalman and Wiener Filters Approach. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_105

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89639-5_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89638-8

  • Online ISBN: 978-3-540-89639-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation