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Link to original content: https://doi.org/10.1023/A:1008017513536
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Improved Diffuse Reflection Models for Computer Vision

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Abstract

There are many computational vision techniques that fundamentally rely upon assumptions about the nature of diffuse reflection from object surfaces consisting of commonly occurring nonmetallic materials. Probably the most prevalent assumption made about diffuse reflection by computer vision researchers is that its reflected radiance distribution is described by the Lambertian model, whether the surface is rough or smooth. While computationally and mathematically a relatively simple model, in physical reality the Lambertian model is deficient in accurately describing the reflected radiance distribution for both rough and smooth nonmetallic surfaces. Recently, in computer vision diffuse reflectance models have been proposed separately for rough, and, smooth nonconducting dielectric surfaces each of these models accurately predicting salient non-Lambertian phenomena that have important bearing on computer vision methods relying upon assumptions about diffuse reflection. Together these reflectance models are complementary in their respective applicability to rough and smooth surfaces. A unified treatment is presented here detailing important deviations from Lambertian behavior for both rough and smooth surfaces. Some speculation is given as to how these separate diffuse reflectance models may be combined.

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Authors and Affiliations

  1. Computer Vision Laboratory, Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218

    Lawrence B. Wolff

  2. Center for Research in Intelligent Systems, Department of Computer Science, Columbia University, New York, NY, 10027

    Shree K. Nayar

  3. Artificial Intelligence Laboratory, Massachusetts, Institute of Technology, Cambridge, MA, 02139

    Michael Oren

Authors
  1. Lawrence B. Wolff
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  2. Shree K. Nayar
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  3. Michael Oren
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Wolff, L.B., Nayar, S.K. & Oren, M. Improved Diffuse Reflection Models for Computer Vision. International Journal of Computer Vision 30, 55–71 (1998). https://doi.org/10.1023/A:1008017513536

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