Pintus, RuggeroAhsan, MoonisaMarton, FabioGobbetti, EnricoHulusic, Vedad and Chalmers, Alan2021-11-022021-11-022021978-3-03868-141-02312-6124https://doi.org/10.2312/gch.20211412https://diglib.eg.org:443/handle/10.2312/gch20211412We 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.Computing methodologiesAppearance and texture representationsReflectance modelingScene understanding10.2312/gch.20211412103-112