Façade Segmentation in a Multi-view Scenario

Abstract

We examine a new method of façade segmentation in a multi-view scenario. A set of overlapping, thus redundant street-side images exists and each image shows multiple buildings. A semantic segmentation identifies primary areas in the image such as sky, ground, vegetation, and façade. Subsequently, repeated patterns are detected in image segments previous labeled as "façade areas" and are applied to separate specific facades from each other. Experimentation is based on an industrial street-view dataset from a moving car by well-designed, calibrated, automated cameras. High overlap images define a multi-view scenario. We achieve 97% pixel-wise segmentation effectiveness, outperforming current state-of-the-art methods.