The ability to perceive the environments in different ways is essential to robotic research. This involves the analysis of both 2D and 3D data sources. We present a large scale urban scene dataset associated with a handy simulator based on Unreal Engine 4 and AirSim, which consists of both man-made and real-world reconstruction scenes in different scales, referred to as UrbanScene3D. Unlike previous works that purely based on 2D information or man-made 3D CAD models, UrbanScene3D contains both compact man-made models and detailed real-world models reconstructed by aerial images. Each building has been manually extracted from the entire scene model and then has been assigned with a unique label, forming an instance segmentation map. The provided 3D ground-truth textured models with instance segmentation labels in UrbanScene3D allow users to obtain all kinds of data they would like to have: instance segmentation map, depth map in arbitrary resolution, 3D point cloud/mesh in both visible and invisible places, etc. In addition, with the help of AirSim, users can also simulate the robots (cars/drones)to test a variety of autonomous tasks in the proposed city environment. Please refer to our paper and website(https://vcc.tech/UrbanScene3D/) for further details and applications.

UrbanScene3D是一个大规模的数据平台，包含128k高清图像和高精度LiDAR扫描数据，提供综合基准来设计和评估空中路径规划和三维重建算法的工具，并能够生成各种数据（如二维深度图，二维/三维边界框和三维点云/网格分割）以及模拟未来研究中的汽车或无人机在提出的城市环境中的运行。

捕获、重建和模拟：UrbanScene3D数据集