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Approximating Reflectance Functions using Neural Networks

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Rendering Techniques ’98 (EGSR 1998)

Part of the book series: Eurographics ((EUROGRAPH))

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Abstract

We present a new representation for the storage and reconstruction of arbitrary reflectance functions. This non-linear representation, based on a neural network model, accurately captures the spectral and spatial variation of these functions. It is both computationally efficient and concise, yet expressive. We reconstruct the subtle reflection characteristics of an analytic reflection model as well as measured and simulated reflection data

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References

  1. Blinn, James. “Models of Light Reflection for Computer Synthesised Pictures,” Computer Graphics, 11(4), July 1977

    Google Scholar 

  2. Cabral, Brian, Max Nelson and Springmeyer Rebecca. “Bi-directional Reflection Functions from Surface Bump Maps,” Computer Graphics, 21(4), July 1987

    Google Scholar 

  3. Cook, Robert L. and Torrence, Kenneth E., “A Reflectance Model for Computer Graphics,” Computer Graphics, 15(3), August 1981

    Google Scholar 

  4. Dana, K.J., Nayar S.K., vam Ginneken Bram and Kooenderink Jan J., “Reflectance and Texture of Real-World Surfaces”, Technical Report, http://www.cs.columbia.edu/CAVE/curet/html

    Google Scholar 

  5. Glassner., Andrew, S., “Principles of Digital Images Synthesis,” The Morgan Kaufmann Series in Computer Graphics and Geometric Modelling. 1995

    Google Scholar 

  6. Gondek, Jay S., Meyer G.W., and Newmann J.G., “Wavelength Dependent Reflectance Functions” Computer Graphics, 28(4), July 1994

    Google Scholar 

  7. He, Xiao D., Torrence Kenneth E., Sillion, François X. and Greenberg Donald P., “A Comprehensive Physical Model for Light Reflection,” Computer Graphics, 25(4), July 1991

    Google Scholar 

  8. Kajiya, James T., “Anisotropic Reflectance Models,” Computer Graphics, 19(4), August 1985

    Google Scholar 

  9. Kajiya, James T., “The Rendering Equation,” Computer Graphics, 20(4), August 1986

    Google Scholar 

  10. Krueger, Wolfgang. “Intensity Fluctuations and Natural Texturing,” Computer Graphics, 22(4), August 1988

    Google Scholar 

  11. Kung., S.Y. Digital Neural Networks. Prentice Hall, 1992

    Google Scholar 

  12. LaFortune, Eric P. and Willems, Yves D., “Using the modified Phong Reflectance Model for Physically Based Rendering,” Report CW 197, Department of Computing Science, K.U. Leuven, November 1994.

    Google Scholar 

  13. LaFortune, Eric P., Foo, Sing-Choong, Torrence, Kenneth E. and Greenberg, Donald P., “Non-Linear Approximation of Reflectance Functions,” Computer Graphics, 31(4), August 1997.

    Google Scholar 

  14. Lalonde, Paul, “Representation and Uses of Light Distribution Functions,” PhD. Thesis, University of British Columbia, December 1997.

    Google Scholar 

  15. Lewis, Robert R., “Making Shaders More Physically Plausible,” In Proceedings of Fourth Eurographics Workshop on Rendering, Paris 1993

    Google Scholar 

  16. Meyer, G.W. “Wavelength Selection for Synthetic Image generation,” Computer Vision, Graphics and Image Processing. No 48, 1998

    Google Scholar 

  17. Neumann, L. “Photosimulation: interreflection with arbitrary reflectance models and illumination,” Computer Graphics Forum 8(1) March 1989

    Google Scholar 

  18. National Institute of Standards and Technology, United States Department of Commerce. Report of Workshop on Advanced Methods and Models for Appearance of Coatings and Coated Objects, March 1997

    Google Scholar 

  19. Oren, Michael and Nayar, Shreek., “Generalisation of Lambert’s Reflectance Model,” Computer Graphics, 22(4), August 1994, 28(4), July 1993

    Google Scholar 

  20. Phong, Bui-Tuong. “Illumination for Computer Generated Images.” Communications of the ACM, 18(6) 1975.

    Google Scholar 

  21. Poulin, Pierre and Fournier, Alain. “A Model for Anisotropic Reflection” Computer Graphics, 24(4), August 1990

    Google Scholar 

  22. Rosenblatt, F., “The perceptron: A probabilistic model for information storage and organisation in the brain,” Psychology Review Vol 65, 1958

    Google Scholar 

  23. Rummelhart, D. E., et. al. “Parallel Distributed Processing (PDP): Exploration in the Microstructure of Cognition,” volume 1. The MIT Press, Cambridge MA, 1986

    Book  Google Scholar 

  24. Sanford, B. and Robertson D., “Infrared Reflectance Properties of aircraft Paints,” Proceedings of the 1985 Meeting of the IRIS Speciality Group on Targets, Backgrounds and Discrimination, February 1985

    Google Scholar 

  25. Schröder, Peter and Sweldens, Wim. “Spherical Wavelets: Efficiently Representing Functions on the Sphere,” Computer Graphics, 29(4), August 1995

    Google Scholar 

  26. Shirley, Peter, Wang, Changyaw and Zimmerman Kurt. “Monte Carlo Techniques for Direct Lighting Calculations,”

    Google Scholar 

  27. Strauss, P. S., “A realistic lighting model for computer animators,” IEEE Computer Graphics & Applications, 10(11) November 1990.

    Google Scholar 

  28. Ward, Gregory J., “Measuring and Modelling Anisotropic Reflection,” Computer Graphics, 26(2), July 1992

    Google Scholar 

  29. Westin, Stephen H., Arvo, James R. and Torrance Kenneth E., “Predicting Reflectance Functions from Complex Surfaces,” Computer Graphics, 26(2), July 1992

    Google Scholar 

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Author information

Authors and Affiliations

  1. Image Synthesis Group, Dept. of Computer Science, Trinity College Dublin, Ireland

    David Gargan & Francis Neelamkavil

Authors
  1. David Gargan
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  2. Francis Neelamkavil
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Editor information

Editors and Affiliations

  1. INRIA, Grenoble, France

    George Drettakis

  2. Lawrence Livermore National Laboratory, University of California, USA

    Nelson Max

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© 1998 Springer-Verlag Wien

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Gargan, D., Neelamkavil, F. (1998). Approximating Reflectance Functions using Neural Networks. In: Drettakis, G., Max, N. (eds) Rendering Techniques ’98. EGSR 1998. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6453-2_3

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  • DOI: https://doi.org/10.1007/978-3-7091-6453-2_3

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83213-4

  • Online ISBN: 978-3-7091-6453-2

  • eBook Packages: Springer Book Archive

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