Symmetries and repetitions are common and valuable features in urban scenes. We propose to leverage such regularity information in an efficient optimization scheme in order to segment a rectified image of a facade into semantic categories. Our method retrieves a parsing which respects common architectural constraints as well as detected repetitive structures and edge information. Additionally, the use of symmetry information allows us to efficiently deal with large occluded areas and to recover plausible facade images with a minimum of occlusions. Our approach yields state-of-The-Art accuracy on datasets with challenging occlusions. Competitive works either fully fail to deal with large occlusions or they are an order of magnitude slower than our approach.