skip to main content
10.1145/2343483.2343502acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
research-article

Advanced (quasi) Monte Carlo methods for image synthesis

Published:05 August 2012Publication History

ABSTRACT

Monte Carlo ray tracing has become ubiquitous in most commercial renderers and in custom shaders used for visual effects and feature animation. But many advanced Monte Carlo algorithms are not widely used and are often misunderstood. In this course, attendees learn about the practical aspects of variance-reduction methods with a focus on all variants of importance sampling. The course also covers quasi-Monte Carlo methods at the industry level, as well as the practical aspects of bidirectional path tracing combined with multiple importance sampling and Metropolis Light Transport. Practical advice is provided throughout the course.

Skip Supplemental Material Section

Supplemental Material

crs124_1_12.mp4

mp4

4.7 MB

crs124_2_12.mp4

mp4

27.2 MB

crs124_3_12.mp4

mp4

53.8 MB

crs124_4_12.mp4

mp4

11.3 MB

References

  1. Ashikhmin, M., Premoze, S., Shirley, P., and Smits, B. A variance analysis of the metropolis light transport algorithm. Computers & Graphics 25, 2 (2001), 287--294.Google ScholarGoogle ScholarCross RefCross Ref
  2. Clarberg, P., and Akenine-Möller, T. Exploiting Visibility Correlation in Direct Illumination. Comp. Graph. Forum (Proc. of EGSR 2008) 27, 4 (2008), 1125--1136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Clarberg, P., and Akenine-Möller, T. Practical Product Importance Sampling for Direct Illumination. Comp. Graph. Forum (Proc. of Eurographics 2008) 27, 2 (2008), 681--690.Google ScholarGoogle Scholar
  4. Clarberg, P., Jarosz, W., Akenine-Möller, T., and Jensen, H. W. Wavelet Importance Sampling: Efficiently Evaluating Products of Complex Functions. ACM Trans. Graph. 24, 3 (2005), 1166--1175. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cline, D., Egbert, P. K., Talbot, J. F., and Cardon, D. L. Two stage importance sampling for direct lighting. In Rendering Techniques 2006: 17th Eurographics Workshop on Rendering (June 2006), pp. 103--114. Google ScholarGoogle Scholar
  6. Coleman, W. Mathematical Verification of a certain Monte Carlo Sampling Technique and Applications of the Technique to Radiation Transport Problems. Nuclear Science and Engineering 32 (1968), 76--81.Google ScholarGoogle ScholarCross RefCross Ref
  7. Hammersley, J. M., and Handscomb, D. C. Monte Carlo Methods. Wiley, New York, N. Y., 1964.Google ScholarGoogle Scholar
  8. Hastings, W. Monte carlo sampling methods using markov chains and their applications. Biometrika 57 (1970), 97--109.Google ScholarGoogle Scholar
  9. Hesterberg, T. Weighted average importance sampling and defensive mixture distributions. Technometrics 37, 2 (May 1995), 185--194.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kalos, M. H., and Whitlock, P. A. Monte Carlo Methods. John Wiley and Sons, New York, N. Y., 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Kelemen, C., Szirmay-Kalos, L., Antal, G., and Csonka, F. A Simple and Robust Mutation Strategy for the Metropolis Light Transport Algorithm. Computer Graphics Forum 21, 3 (Sept. 2002), 531--540.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kollig, T., and Keller, A. Monte Carlo and Quasi-Monte Carlo Methods. Springer-Verlag, 2000, ch. Efficient Bidirectional Path Tracing by Randomized Quasi-Monte Carlo Integration, pp. 290--305.Google ScholarGoogle Scholar
  13. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. Equations of State Calculations by Fast Computing Machine. Journal of Chemical Physics 21 (1953), 1087--1091.Google ScholarGoogle ScholarCross RefCross Ref
  14. Mitchell, D. P. Consequences of stratified sampling in graphics. In Proc. of ACM SIGGRAPH 1996 (1996), Addison-Wesley, pp. 277--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Raab, M., Seibert, D., and Keller, A. Unbiased global illumination with participating media. In Monte Carlo and Quasi-Monte Carlo Methods (2006), A. Keller, S. Heinrich, and H. Niederreiter, Eds., Springer.Google ScholarGoogle Scholar
  16. Spanier, J., and Gelbard, E. M. Monte Carlo Principles and Neutron Transport Problems. Addison-Wesley, New York, N. Y., 1969.Google ScholarGoogle Scholar
  17. Talbot, J., Cline, D., and Egbert, P. Importance resampling for global illumination. In Rendering Techniques 2005: 16th Eurographics Workshop on Rendering (June 2005), pp. 139--146. Google ScholarGoogle Scholar
  18. Veach, E. Robust Monte Carlo Methods for Light Transport Simulation. PhD thesis, Stanford University, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Veach, E., and Guibas, L. Optimally combining sampling techniques for Monte Carlo rendering. In Proc. of ACM SIGGRAPH 1995 (1995), Addison-Wesley, pp. 419--428. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Veach, E., and Guibas, L. Metropolis Light Transport. Computer Graphics 31 (1997), 65--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Woodcock, E., Murphy, T., Hemmings, P., and Longworth, T. Techniques used in the gem code for monte carlo neutronics calculations in reactors and other systems of complex geometry. Proc. Conf. Applications of Computing Methods to Reactor Problems (1965).Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    SIGGRAPH '12: ACM SIGGRAPH 2012 Courses
    August 2012
    1998 pages
    ISBN:9781450316781
    DOI:10.1145/2343483

    Copyright © 2012 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 5 August 2012

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate1,822of8,601submissions,21%

    Upcoming Conference

    SIGGRAPH '24

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader