  In this paper we present SurfaceNet, an approach for estimating
spatially-varying bidirectional reflectance distribution function (SVBRDF)
material properties from a single image. We pose the problem as an image
translation task and propose a novel patch-based generative adversarial network
(GAN) that is able to produce high-quality, high-resolution surface reflectance
maps. The employment of the GAN paradigm has a twofold objective: 1) allowing
the model to recover finer details than standard translation models; 2)
reducing the domain shift between synthetic and real data distributions in an
unsupervised way. An extensive evaluation, carried out on a public benchmark of
synthetic and real images under different illumination conditions, shows that
SurfaceNet largely outperforms existing SVBRDF reconstruction methods, both
quantitatively and qualitatively. Furthermore, SurfaceNet exhibits a remarkable
ability in generating high-quality maps from real samples without any
supervision at training time.
