Datasets › Toronto-3D
Toronto-3D
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
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | Toronto-3D | SCF-Net OA 95.50 | SCF-Net: Learning Spatial Contextual Features for... | leofansq/SCF-Net | 7 | Compare |
| Semantic Segmentation | Toronto-3D L002 | EyeNet oAcc 94.63 | Human Vision Based 3D Point Cloud Semantic Segmentation... | Yacovitch/EyeNet | 5 | Compare |
Papers archive 2025-07-28
6 shown of 6 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 24. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo | 1 | 2 | 26 Oct 2024 | not harvested |
| Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene | 1 | 1 | 30 Jan 2023 | not harvested |
| SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation | 1 | 1 | 19 Jun 2021 | not harvested |
| Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | 1 | 5 | 18 Mar 2020 | not harvested |
| RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds | 9 | 2 | 25 Nov 2019 | ran 1 of 4 samples (3 unverified) |
| PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | 68 | 1 | 7 Jun 2017 | ran 36 of 67 samples (31 unverified; 26 pointer-only for licence) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- Toronto-3D
- Toronto-3D L002
2 variant names, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections