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Fast Radiosity Solutions For Environments With High Average Reflectance

  • Gladimir V. Guimarães Baranoski
  • Randall Bramley
  • Peter Shirley
Conference paper
Part of the Eurographics book series (EUROGRAPH)

Abstract

In radiosity algorithms the average radiance of n Lambertian patches is approximated by solving a linear system with n unknowns. When n is small (i.e. fewer than thousands of patches), general matrix methods like Gauss-Siedel can be used where the explicit n × n matrix can be precomputed and stored [5]. When n is large, progressive techniques are used where the matrix rows or elements are recomputed as needed [4]. When n is very large (i.e. hundreds of thousands of patches), stochastic techniques can avoid computing or storing the n 2 elements of the matrix [10].

Keywords

Chebyshev Polynomial Iteration Matrix Chebyshev Method Innermost Loop Progressive Technique 
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.

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References

  1. 1.
    G. V. Baranoski, R. Bramley, and P. Shirley, Iterative methods for fast radiosity solutions, tech. rep., Indiana University, 1995.Google Scholar
  2. 2.
    R. Barrett, etal., Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, SIAM, Philadelphia, 1 ed., 1994.Google Scholar
  3. 3.
    R. Burden, and J. Faires, Numerical Analysis, Pws-Kent Publishing Company, Boston, 5 ed., 1993.MATHGoogle Scholar
  4. 4.
    M. Cohen, S. Chen, J. Wallace, and D. Greenberg, A progressive refinement approach to fast radiosity image generation, Computer Graphics, 22 (1988), pp. 75–84.CrossRefGoogle Scholar
  5. 5.
    M. Cohen, and D. Greenberg, The hemi-cube: A radiosity solution for complex environments, Computer Graphics, 19 (1985), pp. 31–40.CrossRefGoogle Scholar
  6. 6.
    M. Feda, and W. Purgathofer, Accelerating radiosity by overshooting, in Proc. of the Third Eurographics Rendering Workshop, Consolidation Express, June 1992, pp. 21–32.Google Scholar
  7. 7.
    S. Goertler,M. Cohen, and P. Slusallek, Radiosity and relaxation methods, IEEE Computer Graphics and Applications, 14 (1994), pp. 48–58.CrossRefGoogle Scholar
  8. 8.
    D. Greenberg, Computers and architecture, Scientific American, 264 (1991), pp. 104–109.CrossRefGoogle Scholar
  9. 9.
    L. Hageman, and D. Young, Applied Iterative Methods, Academic Press, New York, 1981.MATHGoogle Scholar
  10. 10.
    L. Neumann, New efficient algorithms with positive definite radiosity matrix, in Proc. of the Fifth Eurographics Rendering Workshop, June 1994, pp. 219–237.Google Scholar
  11. 11.
    Y. Saad, A. Sameh, and P. Saylor, Solving elliptic difference equations on a linear array of processors, SIAM Journal of Scientific and Statistical Computing, 6 (1985), pp. 1049–1063.MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    E. Stiefel, Kernel polynomials in linear algebra and their numerical application, in Further Contributions to the Solutions of Simultaneous Linear Equations and the Determination of Eigenvalues, National Bureau of Standards, Applied Mathematical Series - 49, 1958.Google Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • Gladimir V. Guimarães Baranoski
    • 1
  • Randall Bramley
    • 1
  • Peter Shirley
    • 2
  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.Cornell UniversityIthacaUSA

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