Efficient 2D and 3D Facade Segmentation using Auto-Context - École des Ponts ParisTech Access content directly
Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2018

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.
Fichier principal
Vignette du fichier
PAMI-2017-Gadde-et-al.pdf (6.57 Mo) Télécharger le fichier
PAMI-2017-Gadde-et-al_supp.pdf (19.04 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01743579 , version 1 (26-03-2018)

Identifiers

Cite

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, 2018, 40 (5), pp.1273-1280. ⟨10.1109/TPAMI.2017.2696526⟩. ⟨hal-01743579⟩
428 View
570 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More