Two-layer microfacet model with diffraction

•Based on the CTD model, we add two coefficients—σ and σsd—where σ can be used to adjust brightness and σsd to adjust the effect of diffuse diffraction. By adjusting the two coefficients we make a better approximation to the measured data.•Three techniques are applied in our experiments—precomputation, convolution computation, and the Gauss–Newton method—to reduce the rendering cost and find the best coefficients. [Display omitted] The bidirectional scattering distribution function (BSDF) describes how light is scattered on a surface. Microfacet-based BSDF models assume that surfaces are a collection of randomly oriented microfacets, describe the distribution probabilities of these microfacets, and plausibly approximate their reflective properties using parameterized expressions. Traditional analytic microfacet models, such as Cook–Torrance (CT) and the Oren–Nayar (ON), ignore the effects of diffraction. The Cook–Torrance diffraction model (CTD) combines the traditional Cook–Torrance model with diffraction effects to better approximate the measured reflectance. However, the effects of diffuse diffraction are ignored in this two-layer microfacet reflectance model. In this paper, We propose a two-layer microfacet reflectance model that combines the effects of specular diffraction with those of diffuse diffraction. The upper layer of our model is the Cook–Torrance microfacet model with diffraction, while the lower layer is the Oren–Nayar microfacet model that considers the effects of diffuse diffraction. Our model yields a better approximation than the CTD model, especially for plastic-like multi-layer materials. In contrast to previous models where the effects of diffraction cannot be controlled, those of our model can be easily manipulated by the coefficient of diffraction roughness. The proposed OND model is based on three techniques: precomputation, convolution computation, and the Gauss–Newton method.