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Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constrained nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in andoi:10.1109/cvpr.2016.488 dblp:conf/cvpr/RematasRFGT16 fatcat:5thra7wcyjh7zlwiuzpjpyvac4