Capturing, Reconstructing, and Simulating: The UrbanScene3D Dataset

Liqiang Lin, Yilin Liu, Yue Hu, Xingguang Yan, Ke Xie, Hui Huang

ECCV 2022

doi:10.1007/978-3-031-20074-8_6 /eccv/2022/lin2022eccv-capturing/

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 km2 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 dataset with aerial path planning and 3D reconstruction benchmark is available at: https://vcc.tech/UrbanScene3

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Cite

Text

Lin et al. "Capturing, Reconstructing, and Simulating: The UrbanScene3D Dataset." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20074-8_6

Markdown

[Lin et al. "Capturing, Reconstructing, and Simulating: The UrbanScene3D Dataset." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/lin2022eccv-capturing/) doi:10.1007/978-3-031-20074-8_6

BibTeX

@inproceedings{lin2022eccv-capturing,
  title     = {{Capturing, Reconstructing, and Simulating: The UrbanScene3D Dataset}},
  author    = {Lin, Liqiang and Liu, Yilin and Hu, Yue and Yan, Xingguang and Xie, Ke and Huang, Hui},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2022},
  doi       = {10.1007/978-3-031-20074-8_6},
  url       = {https://mlanthology.org/eccv/2022/lin2022eccv-capturing/}
}