Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs (Unknown language)

We present a practical solution to create a relightable model from Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking few images with a different local illumination. By exploiting information from neighboring pixels through a carefully crafted weighting and regularization scheme, we are able to efficiently infer subtle per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method is qualitatively and quantitatively evaluated on both synthetic data and real paintings in the scope of image-based relighting applications.