Quality evaluation of automatically estimated BRDF models

Abstract The Bidirectional Reflectance Distribution Function (BRDF) is widely used in computer graphics to model material appearance based on light and view directions, though it neglects other dependencies, such as spatial. While numerous parametric BRDF models exist, they vary significantly in complexity and their capacity to fit empirical data. This paper evaluates the automated performance of 21 frequently used BRDF models, focusing on their utility without human tuning. We utilized the Levenberg–Marquardt method for parameter estimation without prior knowledge or post-processing. Our motivation stems from the need for efficient modeling more complex and accurate approximations of material visual appearance in the form of Spatially Varying BRDF (SVBRDF) and Bidirectional Texture Functions (BTF). While SVBRDFs offer lower complexity than full BTFs, both can be effectively combined. Our goal was to compare these models using a fully automatic approach to determine their suitability for SVBRDF or BTF modeling, particularly with restricted angular resolution measurements for both isotropic and anisotropic materials. To ensure accuracy, all tests were conducted using high-quality UTIA BTF measurements (from the MAM2014 sample set). Factual errors were computed directly between measurements and model predictions to eliminate potential contamination from rendering or illumination inaccuracies. Ultimately, we aim to identify the best automatically estimated general model capable of representing a wide variety of materials.