3DHD CityScenes: High-Definition Maps in High-Density Point Clouds

In this paper, we present 3DHD CityScenes - a new dataset with the most comprehensive, large-scale high-definition (HD) map to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. The HD map covers a wide variety of map element types, for instance traffic signs and lights, construction site elements such as cones and fences, markings, lanes, and relations between map elements. Our pre-sented dataset is suitable for numerous perception tasks, such as 3D object detection or map deviation detection. Furthermore, we address the example task of detecting traffic signs in LiDAR point clouds, proposing a novel method based on a deep neural network. Our architecture, named 3DHDNet, specifically allows for the individual detection of vertically stacked signs. 3DHDNet significantly outperforms two state-of-the-art architectures that we selected for comparison. Our method achieves an F 1 score, recall, and precision, of 0.83, 0.76, and 0.90, respectively, and may serve as a baseline for future approaches. The dataset is available at https://www.hi-drive.eu/Data for both commercial and non-commercial use.