Reflectance Capture using Univariate Sampling of BRDFs

We propose the use of a light-weight setup consisting of a collocated camera and light source — commonly found on mobile devices — to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D “univariate” sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical BRDF models. Subsequently, we use a dictionary-based reflectance prior to derive a robust technique for per-pixel normal and BRDF estimation. We demonstrate real-world shape and capture, and its application to material editing and classification, using real data acquired using a mobile phone.