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

TL;DR

This paper presents a fast, domain-independent facade segmentation method for 2D images and 3D point clouds, utilizing auto-context features with boosted decision trees, achieving competitive results efficiently.

Contribution

It introduces a simple, efficient, and domain-independent segmentation approach using auto-context features and boosted decision trees, outperforming or matching previous methods.

Findings

Performs better or comparable to existing methods

Works efficiently on 2D and 3D datasets

Simple to implement and extend

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.