@inproceedings{gehler11nips, title = {Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance}, author = {Gehler, Peter and Rother, Carsten and Kiefel, Martin and Zhang, Lumin and Sch\"{o}lkopf, Bernhard}, booktitle = {Advances in Neural Information Processing Systems (NIPS)}, editor = {Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.}, eprint = {gehler11nips}, pages = {765-773}, abstract = {We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.}, year = {2011} }