# Efficient 2D and 3D Facade Segmentation using Auto-Context Source: [Efficient 2D and 3D Facade Segmentation using Auto-Context]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:22:26.903Z Authors: Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler Published: 2016-06-21T06:50:35.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Point cloud labeling on RueMonge 2014 v1 (test)](): Point cloud labeling; RueMonge 2014 v1 (test); Average IoU: 62.9. - [Multi-view image labeling on RueMonge 2014 (test)](): Multi-view image labeling; RueMonge 2014 (test); Average IoU: 62.7. - [Semantic Segmentation on RueMonge 2014](): Semantic segmentation; RueMonge 2014; mIoU: 54.4. - [3D Semantic Segmentation on RueMonge Varcity TASK3 2014](): 3D Semantic Segmentation; RueMonge Varcity TASK3 2014; mIoU: 54.4. ## Original sources and implementation [Original paper]() [PDF]()