Data-driven Enhancement of SVBRDF Reflectance Data

Analytical SVBRDF representations are widely used to represent spatially varying material appearance depending on view and light configurations. State-of-the-art industry-grade SVBRDF acquisition devices allow the acquisition within several minutes. For many materials with a surface reflectance behavior exhibiting complex effects of light exchange such as inter-reflections, self-occlusions or local subsurface scattering, SVBRDFs cannot accurately capture material appearance. We therefore propose a method to transform SVBRDF acquisition devices to full BTF acquisition devices. To this end, we use data-driven linear models obtained from a database of BTFs captured with a traditional BTF acquisition device in order to reconstruct high-resolution BTFs from the SVBRDF acquisition devices’ sparse measurements. We deal with the high degree of sparsity using Tikhonov regularization. In our evaluation, we validate our approach on several materials and show that BTF-like material appearance can be generated from SVBRDF measurements in the range