SemanticSpray Dataset

LiDARs are one of the main sensors used for autonomous driving applications, providing accurate depth estimation regardless of lighting conditions. However, they are severely affected by adverse weather conditions such as rain, snow, and fog. This dataset provides semantic labels for a subset of the RoadSpray [1] dataset, which contains scenes of vehicles traveling at different speeds on wet surfaces, creating a trailing spray effect. We provide semantic labels for over 200 dynamic scenes, labeling each point in the LiDAR point clouds as background (road, vegetation, buildings, ...), foreground (moving vehicles), and noise (spray, LiDAR artifacts). The dataset toolkit is available at: https://github.com/aldipiroli/semantic_spray_dataset References: [1] C. Linnhoff, L. Elster, P. Rosenberger, and H. Winner, "Road spray in lidar and radar data for individual moving objects," 2022-04. [Online]. DOI: https://doi.org/10.48328/tudatalib-930 Available: https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3537

Faculties

Fakultät für Ingenieurwissenschaften, Informatik und Psychologie

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Institut für Mess-, Regel- und Mikrotechnik

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CC BY 4.0 International

https://creativecommons.org/licenses/by/4.0/

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https://doi.org/10.48328/tudatalib-930

Keywords

Adverse weather conditions, Vehicle road spray, Lidar, Optical radar, DDC 000 / Computer science, information & general works, DDC 004 / Data processing & computer science

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SemanticSpray Dataset

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DFG Project uulm

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