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TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas

: Zhu, Jingwei; Gehrung, Joachim; Huang , Rong; Borgmann, Björn; Hoegner, Ludwig; Zhenghao, Sun; Hebel, Marcus; XU, Yusheng; Stilla, Uwe

Fulltext urn:nbn:de:0011-n-5934791 (27 MByte PDF)
MD5 Fingerprint: 1c05cfda775aba0a16a97876934833b3
Created on: 3.7.2020

Remote sensing 12 (2020), No.11, Art. 1875, 18 pp.
ISSN: 2072-4292
Journal Article, Electronic Publication
Fraunhofer IOSB ()
MLS point clouds; Instance Segmentation

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.