TUM-FAÇADE: REVIEWING AND ENRICHING POINT CLOUD BENCHMARKS FOR FAÇADE SEGMENTATION

Abstract. Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets commonly investigated benchmark various interpretation methods. Yet, few researchers have addressed use benchmarks façade segmentation. Robust segmentation is becoming a key factor in applications ranging from simulating autonomous driving functions to preserving cultural heritage. In this work, we present method enriching existing with façade-related classes that been designed facilitate testing. We propose how efficiently extend and comprehensively assess their potential create TUM-FAÇADE dataset, which extends capabilities TUM-MLS-2016. Not only can development point-cloud-based tasks, but our procedure also be applied enrich further datasets.