Facade Proposals for Urban Augmented Reality

We introduce a novel object proposals method specific to building facades. We define new image cues that measure typical facade characteristics such as semantic, symmetry and repetitions. They are combined to generate a few facade candidates in urban environments fast. We show that our method outperforms state-of-the-art object proposals techniques for this task on the 1000 images of the Zurich Building Database. We demonstrate the interest of this procedure for augmented reality through facade recognition and camera pose initialization. In a very time-efficient pipeline we classify the candidates and match them to a facade references database using CNN-based descriptors. We prove that this approach is more robust to severe changes of viewpoint and occlusions than standard object recognition methods.