Efficient 2D and 3D Facade Segmentation using Auto-Context
This provides an efficient and easy-to-implement solution for building facade analysis, though it is incremental as it adapts standard methods rather than introducing a new paradigm.
The paper tackles facade segmentation in 2D images and 3D point clouds by using a domain-independent system based on boosted decision trees with auto-context features, achieving performance comparable to or better than previous methods on benchmark datasets.
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.