Recovering intrinsic images with a global sparsity prior on reflectance

TitleRecovering intrinsic images with a global sparsity prior on reflectance
Publication TypeConference Paper
Year of Publication2011
AuthorsGehler, PVincent, Rother, C, Kiefel, M, Zhang, L, Schölkopf, B
Conference NameAdvances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
ISBN Number9781618395993

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.

Citation KeyGehler2011