TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation
This work addresses the need for better benchmarks in façade segmentation, which is important for applications like autonomous driving and cultural heritage, but it is incremental as it builds on existing datasets.
The paper tackles the lack of point cloud benchmarks for façade segmentation by proposing a method to enrich existing datasets with façade-related classes, resulting in the creation of the TUM-FAÇADE dataset based on TUM-MLS-2016.
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 façade segmentation. Robust façade 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 façade-related classes that have been designed to facilitate façade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for façade segmentation. We use the method to create the TUM-FAÇADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FAÇADE facilitate the development of point-cloud-based façade segmentation tasks, but our procedure can also be applied to enrich further datasets.