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OpenGF is an Ultra-Large-Scale Ground Filtering dataset covering over 47 km² built upon open ALS point clouds of 4 different countries around the world. It not only includes more than half a billion finely labeled ground and non-ground points, but also contains 9 different terrain scenes.

Details on the dataset can be found at CVPRW2021.

If you have questions, or any suggestions to help us improve the dataset, please contact Nannan Qin.

Dataset can be downloaded at GoogleDrive or 百度网盘(提取码：ybw4). Check Changelog for changes.

OpenGF contains information licensed under the Open Government Licence – Ontario, the CC BY 4.0 & the CC BY-SA 4.0 Licence. Part of this work is based on [data, processing] services provided by the OpenTopography Facility with support from the National Science Foundation under NSF Award Numbers 1948997, 1948994 & 1948857.

At present, the point clouds are stored in laz files to reduce the storage space as much as possible. If you are not familiar with the laz format, it is recommended to use professional tools (such as 'CloudCompare') to convert laz files to other common files (such as 'ply') before subsequent processing.

If it is helpful for your work, please consider citing our paper:

@article{qin2021opengf,
    title = {OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World},
    author = {Qin, Nannan and Tan, Weikai and Ma, Lingfei and Zhang, Dedong and Li, Jonathan},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
    year = {2021}
}

• 11/04/2021: The dataset is available for download!
• 08/04/2021: The OpenGF has been accepted by CVPRW 2021!

1. 3D-Terrain-Recognition: Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition
2. VPNet-CRF: Semantic Labeling of ALS Point Cloud via Learning Voxel and Pixel Representations
3. Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways