The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-2/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 529–536, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-529-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 529–536, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-529-2022
 
25 Feb 2022
25 Feb 2022

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

O. Wysocki, L. Hoegner, and U. Stilla O. Wysocki et al.
  • Photogrammetry and Remote Sensing, TUM School of Engineering and Design, Technical University of Munich (TUM), Munich, Germany

Keywords: Point cloud benchmark, Façade segmentation, Semantic segmentation, Review, TUM-FAÇADE, 3D reconstruction

Abstract. 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.