Connecting measured BRDFs to analytic BRDFs by data-driven diffuse-specular separation

The bidirectional reflectance distribution function (BRDF) is crucial for modeling the appearance of real-world materials. In production rendering, analytic BRDF models are often used to approximate the surface appearance since they are compact and flexible. Measured BRDFs usually have a more realistic appearance, but consume much more storage and are hard to modify. In this paper, we propose a novel framework for connecting measured and analytic BRDFs. First, we develop a robust method for separating a measured BRDF into diffuse and specular components. This is commonly done in analytic models, but has been difficult previously to do explicitly for measured BRDFs. This diffuse-specular separation allows novel measured BRDF editing on the diffuse and specular parts separately. In addition, we conduct analysis on each part of the measured BRDF, and demonstrate a more intuitive and lower-dimensional PCA model than Nielsen et al. [2015]. In fact, our measured BRDF model has the same number of parameters (8 parameters) as the commonly used analytic models, such as the GGX model. Finally, we visualize the analytic and measured BRDFs in the same space, and directly demonstrate their similarities and differences. We also design an analytic fitting algorithm for two-lobe materials, which is more robust, efficient and simple, compared to previous non-convex optimization-based analytic fitting methods.