Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes.
Source: https://github.com/WeikaiTan/Toronto-3D
Image Source: https://github.com/WeikaiTan/Toronto-3D
Variants: Toronto-3D, Toronto-3D L002
This dataset is used in 1 benchmark:
Task | Model | Paper | Date |
---|---|---|---|
3D Semantic Segmentation | KPFCNN | Toronto-3D: A Large-scale Mobile LiDAR … | 2020-03-18 |
3D Semantic Segmentation | TGNet | Toronto-3D: A Large-scale Mobile LiDAR … | 2020-03-18 |
3D Semantic Segmentation | MS-PCNN | Toronto-3D: A Large-scale Mobile LiDAR … | 2020-03-18 |
3D Semantic Segmentation | PointNet++ | Toronto-3D: A Large-scale Mobile LiDAR … | 2020-03-18 |
3D Semantic Segmentation | DGCNN | Toronto-3D: A Large-scale Mobile LiDAR … | 2020-03-18 |
3D Semantic Segmentation | RandLANet | RandLA-Net: Efficient Semantic Segmentation of … | 2019-11-25 |
Recent papers with results on this dataset: