Surface appearance is one of the most important aspects of commercial products in fields ranging from the car industry and consumer electronics to cosmetics. Manufacturers strive to introduce special visual effects (finishing, coating) in order to visually communicate functional properties of products using a value-added, customized product design. However, these effects often feature a complex high-dynamic/high-frequency gonio-apparent visual behavior. For these reasons, perceptually accurate and affordable capturing of such effects for the purposes of visualization, material identification, or quality control tasks is one of the current research challenges in the field. We argue that for each application task there is a material- and application-dependent critical amount of information with respect to material appearance. Therefore, the main goal of this project is the development of novel methods combining physical phenomena modelling and visual perception theory, identifying and capturing appropriate amounts of visual data necessary for individual applications.