A data driven BRDF model based on Gaussian process regression

Data driven bidirectional reflectance distribution function (BRDF) models have been widely used in computer graphics in recent years to get highly realistic illuminating appearance. Data driven BRDF model needs many sample data under varying lighting and viewing directions and it is infeasible to deal with such massive datasets directly. This paper proposes a Gaussian process regression framework to describe the BRDF model of a desired material. Gaussian process (GP), which is derived from machine learning, builds a nonlinear regression as a linear combination of data mapped to a high-dimensional space. Theoretical analysis and experimental results show that the proposed GP method provides high prediction accuracy and can be used to describe the model for the surface reflectance of a material.