Paper-1: Efficient Facade Segmentation using Auto Context

Authors: Varun Jampani*, Raghudeep Gadde* and Peter V. Gehler (*equal contribution)

Abstract: In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using auto-context features and learned using the stacked generalization technique. We find that this, albeit standard, technique performs better, or equals, all previous published empirical results on all available facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test time inference.

Main paper: [pdf]

Supplementary material: [pdf]

Paper-2: Efficient 2D and 3D Facade Segmentation using Auto Context

Authors: Raghudeep Gadde*, Varun Jampani*, Renaud Marlet and Peter V. Gehler (*equal contribution)

Abstract: 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.

arXiv preprint: [pdf]