Image-based appearance acquisition of effect coatings

Paint manufacturers strive to introduce unique visual effects to coatings in order to visually communicate functional properties of products using value-added, customized design. However, these effects often feature complex, angularly dependent, spatially-varying behavior, thus representing a challenge in digital reproduction. In this paper we analyze several approaches to capturing spatially-varying appearances of effect coatings. We compare a baseline approach based on a bidirectional texture function (BTF) with four variants of half-difference parameterization. Through a psychophysical study, we determine minimal sampling along individual dimensions of this parameterization. We conclude that, compared to BTF, bivariate representations better preserve visual fidelity of effect coatings, better characterizing near-specular behavior and significantly the restricting number of images which must be captured.