Reflectance Models with Fast Importance Sampling

Abstract

We introduce a physically plausible mathematical model for a large class of BRDFs. The new model is as simple as the well‐known Phong model, but eliminates its disadvantages. It gives a good visual approximation for many practical materials: coated metals, plastics, ceramics, retro‐reflective paints, anisotropic and retro‐reflective materials, etc. Because of its illustrative properties it can be used easily in most commercial software and because of its low computational cost it is practical for virtual reality. The model is based on a special basic BRDF definition, which meets the requirements of reciprocity and of energy conservation. Then a class of BRDFs is constructed from this basic BRDF with different weight functions. The definition of such weight functions requires the user to specify the profile of the highlights, from which the weight function is obtained by derivation. It is also demonstrated how importance sampling can be used with the new BRDFs.

Physically Based Rendering of Synthetic Objects in Real Environments

“…A complete, general, BRDF parametrization based on the normalized halfway vector was provided by Rusinkiewicz [184]. This parametrization is based on introducing a complementary vector, the difference vector,d, that describes the Neumann et al [163] proposed a class of BRDF models parametrized by the projected difference vector between the incident and outgoing directions, which can be computed by d p = h − (h ⋅ n)n, see figure 6.2c. We study the properties of this parametrization in section 6.2.1 and use it for deriving one of the BRDF models presented in paper H, discussed in section 6.2.2.…”

“…The projected difference vector, d p = h − (h ⋅ n)n, inspired by the relationship to the model of Neumann et al [163] and the observations of measured data.…”

Bidirectional reflectance distribution function measurements and analysis of retroreflective materials

“…The generalized cosine mode from Lafortune et al [34] can model retroreflection using its transformation matrix (using C x C y > 0). Neumann and Neumann's BRDF [35] provides retroreflection using the same transformation mechanism, but lacks a close formulation.…”

Sampling strategies

“…This work was a significant contribution in the context of importance sampling because sampling reflectance distributions, which poses a significant hurdle for realistic materials, was simply reduced to appropriately sampling cosine lobes. Another similar method that unified definitions for a good visual approximation for many materials was presented by Neuman et al [74]. Their model allowed fast importance sampling of physically plausible reflectance functions.…”