We show a learning-based method for low-level vision problems{estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images. We model that world with a Markov network, learning the network parameters from the examples. Bayesian belief propagation allows us to e ciently nd a local maximum of the posterior probability for the scene, given the image...
