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 images 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.

Schematic of different components in our facade segmentation pipeline with a sample facade from ECP dataset. 'Image' and 'Context' features correspond to features extracted on input image and previous-stage segmentation result respectively. 'Stage-n' refers to the nth stage auto-context classifier. The segmentation result is successively refined by the auto-context classifiers from their respective previous stage result.



  title={Efficient facade segmentation using auto-context},
  author={Jampani, Varun and Gadde, Raghudeep and Gehler, Peter V},
  booktitle={Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on},
  title={Efficient 2D and 3D Facade Segmentation using Auto-Context},
  author={Gadde, Raghudeep and Jampani, Varun and Marlet, Renaud and Gehler, Peter V},
  journal={IEEE transactions on pattern analysis and machine intelligence},