Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
This provides a comprehensive platform for researchers in urban scene perception and reconstruction, though it is incremental as it builds on existing simulation tools.
The authors introduced UrbanScene3D, a large-scale dataset with over 128k images and LiDAR scans covering 136 km² of urban scenes, designed to benchmark aerial path planning and 3D reconstruction algorithms.
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.