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Point clouds are widely regarded as one of the best dataset types for urbanmapping purposes. Hence, point cloud datasets are commonly investigated asbenchmark types for various urban interpretation methods. Yet, few researchershave addressed the use of point cloud benchmarks for fa\c{c}ade segmentation.Robust fa\c{c}ade segmentation is becoming a key factor in various applicationsranging from simulating autonomous driving functions to preserving culturalheritage. In this work, we present a method of enriching existing point clouddatasets with fa\c{c}ade-related classes that have been designed to facilitatefa\c{c}ade segmentation testing. We propose how to efficiently extend existingdatasets and comprehensively assess their potential for fa\c{c}adesegmentation. We use the method to create the TUM-FA\c{C}ADE dataset, whichextends the capabilities of TUM-MLS-2016. Not only can TUM-FA\c{C}ADEfacilitate the development of point-cloud-based fa\c{c}ade segmentation tasks,but our procedure can also be applied to enrich further datasets.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

TUM-FA\c{C}ADE: Reviewing and enriching point cloud benchmarks for fa\c{c}ade segmentation