Kavoosighafi, BehnazMantiuk, Rafal K.Hajisharif, SaghiMiandji, EhsanUnger, JonasWang, BeibeiWilkie, Alexander2025-06-202025-06-2020251467-8659https://doi.org/10.1111/cgf.70162https://diglib.eg.org/handle/10.1111/cgf70162Material appearance is commonly modeled with the Bidirectional Reflectance Distribution Functions (BRDFs), which need to trade accuracy for complexity and storage cost. To investigate the current practices of BRDF modeling, we collect the first high dynamic range stereoscopic video dataset that captures the perceived quality degradation with respect to a number of parametric and non-parametric BRDF models. Our dataset shows that the current loss functions used to fit BRDF models, such as mean-squared error of logarithmic reflectance values, correlate poorly with the perceived quality of materials in rendered videos. We further show that quality metrics that compare rendered material samples give a significantly higher correlation with subjective quality judgments, and a simple Euclidean distance in the ITP color space (DEITP) shows the highest correlation. Additionally, we investigate the use of different BRDF-space metrics as loss functions for fitting BRDF models and find that logarithmic mapping is the most effective approach for BRDF-space loss functions.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Perception; Reflectance modeling; RenderingComputing methodologies → PerceptionReflectance modelingRendering10.1111/cgf.7016213 pages