  We present an unsupervised approach for factorizing object appearance into
highlight, shading, and albedo layers, trained by multi-view real images. To do
so, we construct a multi-view dataset by collecting numerous customer product
photos online, which exhibit large illumination variations that make them
suitable for training of reflectance separation and can facilitate object-level
decomposition. The main contribution of our approach is a proposed image
representation based on local color distributions that allows training to be
insensitive to the local misalignments of multi-view images. In addition, we
present a new guidance cue for unsupervised training that exploits synergy
between highlight separation and intrinsic image decomposition. Over a broad
range of objects, our technique is shown to yield state-of-the-art results for
both of these tasks.
