Phenomenological modeling of surface roughness for predicting reflection from targets

Lawrence B. Wolff

doi:10.1117/12.721312

7 May 2007 Phenomenological modeling of surface roughness for predicting reflection from targets

Lawrence B. Wolff

Proceedings Volume 6566, Automatic Target Recognition XVII; 656602 (2007) https://doi.org/10.1117/12.721312
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Exact wave theories of specular reflectance from rough surfaces are computationally intractable thus motivating the practical need for geometric reflectance models which treat only the geometric ray nature of light reflection. The cornerstone of geometric reflectance modeling from rough surfaces in computer vision and computer graphics over the past two decades has been the Torrance-Sparrow model. This model has worked well as an intuitive description of rough surfaces as a collection of planar Fresnel reflectors called microfacets together with the concept of geometric attenuation for light which is obscured during reflection under an assumed rough surface geometry. Experimental data and analysis show that the current conceptualization of how specularly reflected light rays geometrically interact with rough surfaces needs to be seriously revised. The Torrance-Sparrow model while in qualitative agreement with specular reflection from rough surfaces is seen to be quantitatively inaccurate. Furthermore there are conceptual inconsistencies upon which derivation of this reflectance model is based. We show how significant quantitative improvement can be achieved for a geometric reflectance model by making some fundamental revisions to notions of microfacet probability distributions and geometric attenuation. Work is currently undergoing, to relate physical surface reconstructions from Atomic Force Microscope data to reflectance data from these same surfaces.

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Lawrence B. Wolff "Phenomenological modeling of surface roughness for predicting reflection from targets", Proc. SPIE 6566, Automatic Target Recognition XVII, 656602 (7 May 2007); https://doi.org/10.1117/12.721312

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KEYWORDS

Reflectivity

Specular reflections

Visual process modeling

Reflection

Data modeling

Signal attenuation

Atomic force microscopy

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