  We propose a data-driven approach for intrinsic image decomposition, which is
the process of inferring the confounding factors of reflectance and shading in
an image. We pose this as a two-stage learning problem. First, we train a model
to predict relative reflectance ordering between image patches (`brighter',
`darker', `same') from large-scale human annotations, producing a data-driven
reflectance prior. Second, we show how to naturally integrate this learned
prior into existing energy minimization frameworks for intrinsic image
decomposition. We compare our method to the state-of-the-art approach of Bell
et al. on both decomposition and image relighting tasks, demonstrating the
benefits of the simple relative reflectance prior, especially for scenes under
challenging lighting conditions.
