Toronto-3D
Introduced by Tan et al. in Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
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-3DBenchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
|---|---|---|---|---|---|
|
Toronto-3D L002
|
SCF-Net
|
Papers
| Paper | Code | Results | Date | Stars |
|---|
