December 2017
Volume 17, Issue 15
Open Access
OSA Fall Vision Meeting Abstract  |   December 2017
Visual estimation of surface BRDF
Author Affiliations
  • James Ferwerda
    Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology
Journal of Vision December 2017, Vol.17, 41-42. doi:
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      James Ferwerda; Visual estimation of surface BRDF. Journal of Vision 2017;17(15):41-42.

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      © ARVO (1962-2015); The Authors (2016-present)

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The bi-directional reflectance distribution function (BRDF) is a radiometric representation used to describe the reflectance properties of an opaque surface. BRDF features such as the diffuse and specular reflection are associated with surface appearance properties such as color and gloss. BRDF data are often approximated with light reflection models. For example the Ward model characterizes the BRDF with three parameters, ρd, the diffuse reflectance of the surface, ρs, the specular reflectance of the surface, and α, the standard deviation of the surface slope. Recent advances in our understanding of material perception have benefited greatly from photorealistic computer graphics methods, but acquiring the BRDF surface parameters required by these methods has been difficult. In this project we present a method for visually estimating the Ward model parameters of real-world surfaces using a smartphone. We estimate ρd by scaling and linearizing the RGB values extracted from an image of the surface. We estimate ρs by applying the Fresnel equation. Finally, we estimate α by displaying a square wave grating on the smartphone screen, reflecting the grating in the surface under consideration, and adjusting the spatial frequency of the grating until it is just visible. An algorithm that incorporates display characteristics, human contrast sensitivity, and viewing geometry allows us to estimate α from the grating spatial frequency at threshold. We validate of the method by measuring real surfaces, simulating the surfaces using parameters estimated through the method, and comparing images of the real and simulated surfaces.


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