Unsupervised Facade Segmentation Using Repetitive Patterns

We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting–based matcher. In the experiments we compare our approach to extended state–of–the–art matching approaches using more than 600 challenging streetside images, including different building styles and various occlusions. Our algorithm outperforms these approaches and allows to correctly separate 94% of the facades. Pixel–wise comparison to our ground–truth yields a segmentation accuracy of 85%. According to these results our work is an important contribution to fully automatic building reconstruction.