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Article dans une revue

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

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.
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Contributeur : Renaud Marlet Connectez-vous pour contacter le contributeur
Soumis le : lundi 26 mars 2018 - 15:16:42
Dernière modification le : mardi 18 janvier 2022 - 10:30:02
Archivage à long terme le : : jeudi 13 septembre 2018 - 09:11:08



Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter Gehler. Efficient 2D and 3D Facade Segmentation using Auto-Context. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2018, 40 (5), pp.1273-1280. ⟨10.1109/TPAMI.2017.2696526⟩. ⟨hal-01743579⟩



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