A neuro-evolutionary unbiased global illumination algorithm

  • Eduardo Bustillo
Conference paper
Part of the Eurographics book series (EUROGRAPH)


In this paper we present a two pass unbiased global illumina­tion rendering algorithm. First pass calculations are done shooting rays from light sources and storing directional information in a growing adap­tive neural gas structure. The second pass is a ray tracing process which uses this information to create an evolving population of rays that tend to optimally sample their surroundings. Finally, weighted Monte Carlo integration is used with a dynamic Voronoi diagram to reduce uncer­tainty in the solution.


Voronoi Diagram Importance Sampling Bidirectional Reflection Distribution Function Global Illumination Importance Function 
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  1. 1.
    T. Back, F. Hoffmeister, and H. P. Schwefel. A survey of evolution strategies. In R. K. Belew and L. B. Booker, editors, (Proceedings of the Fourth International Conference on Genetic Algorithms), pages 2–9, San Mateo, CA, 1991. Morgan Kaufmann.Google Scholar
  2. 2.
    Markus Beyer and Brigitta Lange. Rayvolution: An Evolutionary Ray Tracing Algorithm. Fifth Eurographics Workshop on Rendering, pages 137–146, June 1994.Google Scholar
  3. 3.
    Christian-A. Bohn. Efficiently Representing the Radiosity Kernel through Learning. Rendering Techniques ‘86 (Proceedings of the Seventh Eurographics Workshop on Rendering), pages 123–132, 1996.Google Scholar
  4. 4.
    J. D. Boissonnat and Monique Teillaud. On the randomized construction of the Delaunay tree. Technical report 1140, INRIA, Valbonne, France, 1989.Google Scholar
  5. 5.
    Eduardo Bustillo. Visualizacion fotorrealistica por ordenador de objetos 3D mediante tecnicas de radiosidad y ray tracing. Graduation thesis 1.0.94. 112, ETS Ingenieros Industriales, Bilbao, Spain, 1994.Google Scholar
  6. 6.
    Eduardo Bustillo. Inteligencia artificial, algoritmos geneticos y teoria de fractales en el analisis bursatil. Internal report, EBI, Getxo, Spain, 1995.Google Scholar
  7. 7.
    Shenchang Eric Chen, Holly E. Rushmeier, Gavin Miller, and Douglass Turner. A Progressive Multi-Pass Method for Global Illumination. Computer Graphics (ACM SIGGRAPH ‘81 Proceedings), 25 (4): 164–174, July 1991.Google Scholar
  8. 8.
    Michael Cohen, Shenchang Eric Chen, John R. Wallace, and Donald P. Greenberg. A Progressive Refinement Approach to Fast Radiosity Image Generation. Computer Graphics (ACM SIGGRAPH ‘88 Proceedings), 22 (4): 75–84, August 1988.CrossRefGoogle Scholar
  9. 9.
    Philip Dutre. Mathematical Frameworks and Monte Carlo Algorithms for Global Illumination in Computer Graphics. Ph.D. thesis, September 1996.Google Scholar
  10. 10.
    B. Fritzke. Fast learning with incremental rbf networks. Neural Processing Letters, 1 (1): 2–5, 1994.CrossRefGoogle Scholar
  11. 11.
    Bernd Fritzke. A growing neural gas network learns topologies. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7. MIT Press, Cambridge, MA, 1995.Google Scholar
  12. 12.
    Cindy M. Goral, Kenneth E. Torrance, Donald P. Greenberg, and Bennett Battaile. Modelling the Interaction of Light Between Diffuse Surfaces. Computer Graphics (ACM SIGGRAPH ‘84 Proceedings), 18 (3): 212–222, July 1984.Google Scholar
  13. 13.
    Steven J. Gortler, Peter Schroder, Michael F. Cohen, and Pat Hanrahan. Wavelet Radiosity. Computer Graphics Proceedings, Annual Conference Series, 1993 (ACM SIGGRAPH ‘83 Proceedings), pages 221–230, 1993.Google Scholar
  14. 14.
    Pat Hanrahan, David Salzman, and Larry Aupperle. A Rapid Hierarchical Radiosity Algorithm. Computer Graphics (ACM SIGGRAPH ‘81 Proceedings), 25 (4): 197–206, July 1991.CrossRefGoogle Scholar
  15. 15.
    Dave S. Immel, Michael Cohen, and Donald P. Greenberg. A Radiosity Method for Non-Diffuse Environments. Computer Graphics (ACM SIG-GRAPH ‘86 Proceedings), 20 (4): 133–142, August 1986.CrossRefGoogle Scholar
  16. 16.
    Henrik Wann Jensen. Importance Driven Path Tracing Using the Photon Map. In P. M. Hanrahan and W. Purgathofer, editors, Rendering Techniques ‘85 (Proceedings of the Sixth Eurographics Workshop on Rendering), pages 326–335, New York, NY, 1995. Springer-Verlag.Google Scholar
  17. 17.
    Henrik Wann Jensen. Global Illumination Using Photon Maps. Rendering Techniques ‘86 (Proceedings of the Seventh Eurographics Workshop on Rendering), pages 21–30, 1996.Google Scholar
  18. 18.
    James T. Kajiya. The Rendering Equation. Computer Graphics (ACM SIGGRAPH ‘86 Proceedings), 20 (4): 143–150, August 1986.CrossRefGoogle Scholar
  19. 19.
    Eric P. Lafortune and Yves D. Willems. Bi-directional Path Tracing. Proceedings of Third International Conference on Computational Graphics and Visualization Techniques (Compugraphics ‘83), pages 145–153, December 1993.Google Scholar
  20. 20.
    Eric P. Lafortune and Yves D. Willems. A 5D Tree to Reduce the Variance of Monte Carlo Ray Tracing. In P. M. Hanrahan and W. Purgathofer, editors, Rendering Techniques ‘85 (Proceedings of the Sixth Eurographics Workshop on Rendering), pages 11–20, New York, NY, 1995. Springer—Verlag.Google Scholar
  21. 21.
    T. M. Martinetz and K. J. Schulten. Topology representing networks. Neural Networks, 7 (3): 507–522, 1994.CrossRefGoogle Scholar
  22. 22.
    J. A. Orenstein. Multidimensional trees used for associative searching. Information Processing Letters, 14 (4): 150–157, 1982.CrossRefGoogle Scholar
  23. 23.
    Sumanta N. Pattanaik. Computational Methods for Global Illumination and Visualisation of Complex 3D Environments. Ph.D. thesis, February 1993.Google Scholar
  24. 24.
    Eric Veach and Leonidas Guibas. Bidirectional Estimators for Light Transport. Fifth Eurographics Workshop on Rendering, pages 147–162, June 1994.Google Scholar
  25. 25.
    S. Yakowitz, J. E. Krimmel, and F. Szidarovszky. Weighted Monte Carlo integration. SIAM Journal of Numerical Analysis, 15 (5): 1289–1300, 1978.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Eduardo Bustillo
    • 1
  1. 1.GetxoSpain

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