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HRHD-HK Point Cloud Dataset_0830.zip (4.19 GB)

Supporting data for "HRHD-HK: A Benchmark Dataset of High-Rise and High-Density Urban Scenes for 3D Semantic Segmentation of Photogrammetric Point Clouds"

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Version 2 2023-08-31, 01:27
Version 1 2023-07-19, 03:21
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posted on 2023-08-31, 01:27 authored by Maosu Li, Yijie WuYijie Wu, Anthony Gar On YehAnthony Gar On Yeh, Fan XueFan Xue

HRHD-HK: A Benchmark Dataset of High-Rise and High-Density Urban Scenes for 3D Semantic Segmentation of Photogrammetric Point Clouds

This is the official repository of the HRHD-HK dataset. For technical details, please refer to:

Li, M., Wu, Y., Yeh, A. G. O., & Xue, F. (2023). HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloud. Proceedings of 2023 IEEE International Conference on Image Processing Challenges and Workshops, 3714-3718. IEEE. https://doi.org/10.1109/ICIPC59416.2023.10328383

Overview of HRHD-HK

This paper presents a benchmark dataset of high-rise high-density urban point clouds, namely High-Rise, High-Density urban scenes of Hong Kong (HRHD-HK) for 3D semantic segmentation.

  • The semantic labels of HRHD-HK include 1) building, 2) vegetation, 3) road, 4) waterbody, 5) facility, 6) terrain, and 7) vehicle.
  • Point clouds of HRHD-HK were collected in HK with two features, i.e., color and coordinates in the HK 1980 Grid system (EPSG:2326).
  • HRHD-HK arranged in 150 tiles, contains approximately 273 million points, covering 9.375 km2.
  • Each tile of point clouds was saved in the "ply" format with seven channels, i.e., x, y, z, red, green, blue, and label.
  • HRHD-HK aims to supplement the existing benchmark datasets with Asian HRHD urban scenes as well as subtropical natural landscapes, such as sea, vegetation, and mountains.

For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.

Please cite our paper, if you find our work useful for your research.


Funding

This study was supported in part by the Hong Kong Research Grant Council (RGC) (27200520) and Department of Science and Technology of Guangdong Province (GDST) (2020B1212030009, 2023A1515010757).

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