, Volume 55, Issue 1, pp 23–42 | Cite as

Monte Carlo radiosity

  • L. Neumann


The fast radiosity-type methods for very complex diffuse environments, introduced herein, present a nearly linear-time solution. The outlined procedures rely on recursive algorithms with stochastic convergence for solving the radiosity equation system. Approximations of gathering and shooting at very low computational cost—rather than the exact matrix of a single reflection—are used. The efficiency of the methods will be increased by applying variance reduction techniques.

Key words

Radiosity Monte Carlo algorithms stochastic convergence transillumination method stochastic shooting method variance reduction 

Monte Carlo Radiosity


Die vorgestellten neuen Radiosity Methoden für diffuse Szenen sind besonders geeignet, um die Lichtausbreitung in sehr komplexen Umgebungen in linearer Zeit zu berechnen. Die Verfahren beruhen auf rekursiven Algorithmen, die das Radiosity-Gleichungssystem mittels stochastischer Konvergenz löseu. Approximationen der ‘gathering-’ und ‘shooting-’ Verfahren werden statt einer exakten Berechnung jedes Reflexionsschrittes verwendet. Die Effizienz der Verfahren kann durch geeignete Varianzreduktionsmethoden verbessert werden.


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  1. [1]
    Baum, D., Rushmeier, H. E., Winget, J. M.: Improving radiosity solutions through the use of analytically determined form factors. Proceedings of SIGGRAPH' 89. Comput. Graphics23, 325–334 (1989).Google Scholar
  2. [2]
    Cohen, M. F., Shenchang, E. Ch., Wallace, J. R., Greenberg, D. P.: A Progressive refinement approach to fast radiosity image generation. Proceedings of SIGGRAPH' 88. Comput. Graphics22, 75–84 (1988).Google Scholar
  3. [3]
    Chalmers, A. Paddon, D.: Parallel processing of progressive refinement radiosity. Second Eurographics Workshop on Rendering, Barcelona, Spain, 13–15 May, 1991.Google Scholar
  4. [4]
    Feda, M. Purgathofer, W.: Accelerating radiosity by overshooting. In: Third Eurographics Workshop on Rendering, Bristol, England, 17–20 May, 1992.Google Scholar
  5. [5]
    Gortler, S. J., Cohen, M. F.: Solving the radiosity linear system. In: Communication with virtual worlds (Thalmann, N. M., Thalman, D., eds.), pp. 78–88. Berlin, Heidelberg, New York, Tokyo: Springer 1993.Google Scholar
  6. [6]
    Hanrahan, P., Salzman, D., Aupperle, L.: A rapid hierarchical radiosity algorithm. Proceedings of SIGGRAPH' 91. Comput. Graphics25, 197–206 (1991).Google Scholar
  7. [7]
    Kelemen, C.: Private Communication, 1992.Google Scholar
  8. [8]
    Kalos, M. H., Whitlock, P. A.: Monte Carlo methods, Vol. 1: Basics. New York: Wiley 1986.Google Scholar
  9. [9]
    Niederreiter, H.: Quasi-Monte-Carlo methods and pseudo-random numbers. Bull. Am. Math. Soc.84, 957–1041 (1978).Google Scholar
  10. [10]
    Neumann, L., Neumann, A.: Photosimulation: interreflection with arbitrary reflectance models and illumination. Comput. Graphics Forum8, 21–34 (1989).Google Scholar
  11. [11]
    Neumann, L., Neumann, A.: Radiosity and hybrid methods, 1989, Budapest (accepted by ACM TOG in 1991 and to be published in 1995).Google Scholar
  12. [12]
    Rubinstein, R. Y.: Simulation and the Monte Carlo method. New York: Wiley 1981.Google Scholar
  13. [13]
    Rushmeier, H. E.: Extending the Radiosity method to transmitting and specularly reflecting surfaces. Masters Thesis, Cornell University Ithaca, 1986.Google Scholar
  14. [14]
    Wallace, J. R., Elmquist, K. A., Haines, E. A.: A ray tracing algorithm for progressive radiosity. Proceedings of SIGGRAPH' 89. Comput. Graphics23, 315–324 (1989).Google Scholar
  15. [15]
    Wyszecky, G., Stiles, W. S.: Color science concepts and methods, quantitative data formulae, 2nd ed. New York: Wiley 1982.Google Scholar
  16. [16]
    Young, D. M.: Iterative solution of large linear systems. New York: Academic Press 1971.Google Scholar
  17. [17]
    Neumann, L., Feda, M., Kopp, M., Purgathofer, W.: A new stochastic radiosity method for highly complex scenes. Proc. of the 5th Eurographics Workshop on Rendering, Darmstadt, Germany, 13–15 June, 1994.Google Scholar
  18. [18]
    Neumann, L., Neumann, A., Purgathofer, W., Tobler, R. F., Elias, P., Feda, M., Pueyo, A.: The stochastic ray method for radiosity, Technical Report, TR-186-2-94-17, Technical University of Vienna, Austria, November 1994.Google Scholar

Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • L. Neumann
    • 1
  1. 1.BudapestHungary

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