Papers › Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li, Jing Du, Guorong Cai, Ke Yang, Jonathan Li
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released to encourage new research, and the labels will be improved and updated with feedback from the research community.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | Toronto-3D | KPFCNN | OA | 91.71 | #3 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | KPFCNN | mIoU | 60.30 | #3 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | TGNet | OA | 91.64 | #4 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | TGNet | mIoU | 58.34 | #4 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | MS-PCNN | OA | 91.53 | #5 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | MS-PCNN | mIoU | 58.01 | #5 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | PointNet++ | OA | 91.21 | #6 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | PointNet++ | mIoU | 56.55 | #6 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | DGCNN | OA | 89.00 | #7 of 7 | Archive leaderboard | report |
| 3D Semantic Segmentation | Toronto-3D | DGCNN | mIoU | 49.60 | #7 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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