HyperAI

Toronto-3D City Road Semantic Segmentation Dataset

Date

2 years ago

Size

1.07 GB

Organization

Publish URL

github.com

License

其他

特色图像

Toronto-3D is a large-scale urban outdoor point cloud dataset for semantic segmentation. This large-scale labeled dataset is acquired by the Toronto MLS system in Canada, covers approximately 1 km of point cloud, and consists of approximately 78.3 million points with 8 labeled object classes.

Toronto-3D.torrent
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  • Toronto-3D/
    • README.md
      1011 字节
    • README.txt
      1.97 KB
      • data/
        • Toronto_3D.zip
          1.07 GB