Parametrization of Measured BRDF for Flexible Material Editing

Finding a low dimensional parametric representation of measured BRDF remains challenging. Currently available solutions are either not usable for editing, or rely on limited analytical solutions, or require expensive test subject based investigations. In this work, we strive to establish a parametrization space that affords the data-driven representation variance of measured BRDF models while still offering some of the artistic control of parametric analytical BRDFs. We present a machine learning approach that generates a parameter space relying on a compressed representation of the measured BRDF data. Our solution allows more flexible and controllable material editing possibilities than current machine learning solutions. Finally, we provide a rendering interface, for interactive material editing and interpolation based on the presented new parametrization system.