  We present UrbanScene3D, a large-scale data platform for research of urban
scene perception and reconstruction. UrbanScene3D contains over 128k
high-resolution images covering 16 scenes including large-scale real urban
regions and synthetic cities with 136 km^2 area in total. The dataset also
contains high-precision LiDAR scans and hundreds of image sets with different
observation patterns, which provide a comprehensive benchmark to design and
evaluate aerial path planning and 3D reconstruction algorithms. In addition,
the dataset, which is built on Unreal Engine and Airsim simulator together with
the manually annotated unique instance label for each building in the dataset,
enables the generation of all kinds of data, e.g., 2D depth maps, 2D/3D
bounding boxes, and 3D point cloud/mesh segmentations, etc. The simulator with
physical engine and lighting system not only produce variety of data but also
enable users to simulate cars or drones in the proposed urban environment for
future research.
