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