  This paper introduces a fast and efficient segmentation technique for 2D
images and 3D point clouds of building facades. Facades of buildings are highly
structured and consequently most methods that have been proposed for this
problem aim to make use of this strong prior information. Contrary to most
prior work, we are describing a system that is almost domain independent and
consists of standard segmentation methods. We train a sequence of boosted
decision trees using auto-context features. This is learned using stacked
generalization. We find that this technique performs better, or comparable with
all previous published methods and present empirical results on all available
2D and 3D facade benchmark datasets. The proposed method is simple to
implement, easy to extend, and very efficient at test-time inference.
