Advertisement

An Error Metric for Monte Carlo Ray Tracing

  • Mark R. Bolin
  • Gary W. Meyer
Part of the Eurographics book series (EUROGRAPH)

Abstract

A method is presented for characterizing the error in Monte Carlo realistic image synthesis calculations. An error metric has been developed that can be used to control the variance in the final picture by choosing both the number of rays to be cast into the image plane and the number of rays to be spawned at each bounce in the environment. The method provides specific guidance in how to apply Russian Roulette and splitting at each level of the ray tree. An initial implementation of the method has been done to test the theory and to illustrate its mechanics.

Keywords

Image Plane Surface Intersection Contrast Sensitivity Function Image Synthesis Russian Roulette 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arvo, J. and Kirk D., “Particle Transport and Image Synthesis,” Computer Graphics, Annual Conference Series, ACM SIGGRAPH, pp. 63–66, 1990.Google Scholar
  2. 2.
    Arvo, J., Torrance, K., and Smits B., “A Framework for the Analysis of Error in Global Illumination Algorithms,” Computer Graphics, Annual Conference Series, ACM SIGGRAPH, pp. 75–84, 1994.Google Scholar
  3. 3.
    Bolin, M. R. and Meyer G. W., “A Frequency Based Ray Tracer,” Computer Graphics, Annual Conference Series, ACM SIGGRAPH, pp. 409–418, 1995.Google Scholar
  4. 4.
    Cook, R. L., “Stochastic Sampling in Computer Graphics,” ACM Transactions on Graphics, Vol 5, No 1, pp. 51–72, 1986.Google Scholar
  5. 5.
    Daly, S., “Application of a Noise Adaptive Contrast Sensitivity Function to Image Data Compression,” SPIE Human Vision, Visual Processing and Digital Display, Vol 1077, pp. 217–227, 1989.Google Scholar
  6. 6.
    Kajiya, J. T., “The Rendering Equation,” Computer Graphics, Annual Conference Series, ACM SIGGRAPH, pp. 143–150, 1986.Google Scholar
  7. 7.
    Kahn, H., “Use of Different Monte Carlo Sampling Techniques,” Symposium on Monte Carlo Methods, Wiley: New York pp. 146–190, 1956.Google Scholar
  8. 8.
    Lee, M. E., Redner, R. A., and Uselton, S., “Statistically Optimized Sampling for Distributed Ray Tracing,” Computer Graphics, Annual Conference Series, ACM SIGGRAPH, pp. 61–68, 1985.Google Scholar
  9. 9.
    Meyer, G. W., Rushmeier, H. E., Cohen, M. F., Greenberg, D. P., and Torrance, K. E., “An experimental evaluation of computer graphics imagery,” ACM Transactions on Graphics, Vol. 5, No. 1 pp. 30–50, 1986.CrossRefGoogle Scholar
  10. 10.
    Mikhailov, G. A., Monte Carlo Methods, Springer-Verlag: Berlin Heidelberg 1992.Google Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Mark R. Bolin
    • 1
  • Gary W. Meyer
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of OregonEugeneUSA

Personalised recommendations