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Approximating Reflectance Functions using Neural Networks

  • David Gargan
  • Francis Neelamkavil
Conference paper
Part of the Eurographics book series (EUROGRAPH)

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

Keywords

Activation Function Computer Graphic Hide Unit Reflectance Model Reflectance Function 
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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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • David Gargan
    • 1
  • Francis Neelamkavil
    • 1
  1. 1.Image Synthesis Group, Dept. of Computer ScienceTrinity College DublinIreland

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