Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset (2107.04286v3)

Abstract: 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.

• The paper introduces UrbanScene3D, a comprehensive dataset featuring over 128,000 high-resolution images and 16 diverse urban settings from real and synthetic sources.
• The paper demonstrates that using high-detail proxy representations enhances 3D reconstruction accuracy and completeness in urban scene analyses.
• The paper validates its dataset by benchmarking multiple drone path planning algorithms over 130 flight paths, paving the way for improved urban simulations.

Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset

The paper introduces UrbanScene3D, a comprehensive urban scene dataset that extends the research landscape in urban scene perception, reconstruction, and drone path planning. The dataset includes over 128,000 high-resolution images and 16 diverse scenes, including both real-world and synthetic urban environments. Leveraging Unreal Engine and AirSim, UrbanScene3D facilitates high-fidelity simulations of urban settings, which enhances its utility for researchers working on urban scene understanding. The dataset's extensive scale—covering both large urban areas and intricate city models—distinguishes it from existing resources in this domain.

The authors of UrbanScene3D emphasize its application for aerial path planning through thorough evaluations of different planners using both synthetic and real-world settings. This involves deploying multiple planning algorithms, such as those proposed by Smith et al., Zhou et al., and Zhang et al., each optimized for various operational constraints and objectives like energy efficiency or reconstruction accuracy. The dataset's benchmark includes over 130 flight paths across varying conditions, providing a robust platform for comparing and enhancing path planning methodologies.

Of particular note is the dataset's provision of several proxy representations for the urban environments at varying levels of detail. This enables nuanced analyses of path planning strategies and their effects on 3D reconstruction quality. The paper presents comprehensive numerical results that highlight the impact of using finer proxies on reconstruction completeness and accuracy, confirming that higher detail in proxies generally enhances the fidelity of reconstruction efforts.

UrbanScene3D's implications extend into several theoretical and practical domains. It introduces novel opportunities for advancing algorithms in multi-view stereo reconstruction, depth estimation, and neural rendering tasks. Simulators integrated with real-world scenes, equipped with detailed physical interactions and diverse environmental conditions, can significantly accelerate the development of robust and transferable autonomous systems.

The dataset's path planning benchmarks are particularly instrumental for the development of heuristic functions and real-time planning algorithms that optimize drone navigation for complete urban scene coverage. This aligns with emerging needs in urban environments where UAV exploration must balance efficiency, safety, and data richness.

Looking ahead, this dataset could serve as a foundational tool for the incorporation of high-level geometric features like structural points and wire-frames. Such enhancements could pave the way for more intricate applications in digital twin simulations and real-time monitoring of urban dynamics. The authors' commitment to growing this dataset suggests a potentially expanding role for UrbanScene3D in future advancements in urban informatics and computational modeling.