Automotive
Toronto-3D
Pointwise urban LiDAR semantic segmentation
Vehicle-collected, coloured urban point clouds divided into four spatial sections with eight semantic classes.
Access links
Primary places to inspect, download, read about, or reproduce the dataset.
Quick facts
- Vehicle type
- Automotive
- Environment
- Outdoor
- Data origin
- Real-world
- Sensor count
- 1
- Ground truth
- Not reported
- Calibration
- Not reported
- Annotations
- Available
Sensor overview
High-level sensor availability before the detailed sensor records below.
Ground truth
Reference scope, method, rate, coverage, and provenance.
Not reported
Calibration and synchronization
Reported calibration level, reproducibility signals, and supporting notes.
- Level
- Not reported
- Processed parameters
- Not reported
- Raw calibration data
- Not reported
- Calibration targets
- Not reported
Calibration data
Calibration reproducibility details are not reported.
Notes
Not reportedKnown or intentional limitations
Reported constraints and characteristics to check before using the dataset.
Four adjacent spatial sections cover approximately 1 km of one Toronto roadway and about 78.3 million points. These tiles are not four independent recording sequences.
Released PLY attributes include RGB and GPS time; they do not establish independently released raw images, GNSS observations or pose references.
Large UTM coordinates should be offset before float-based subsampling to avoid precision loss. Moving vehicles can have colour-assignment and acquisition artifacts.
Eight labelled classes plus unclassified are provided. Labels have been revised since the original benchmark; fine road markings and wires remain challenging.
Scanner cadence, raw sensor streams, distributed calibration recordings and independent reference accuracy remain unverified.
Cameras
Not reported
IMUs
Not reported
GNSS
Not reported
LiDAR
Teledyne Optech Maverick MLS LiDAR
- Model or name
- Teledyne Optech Maverick MLS LiDAR
- Count
- 1
- Dimensions
- 3
- Rate
- Not reported
- Channels
- 32
- Range
- Not reported
Radar
Not reported
Additional sensors
Not reported
Annotations
Reported annotation availability and task support.
- Availability
- Available
- Format
- Pointwise PLY labels 0–8; zero unclassified.
- Class count
- 8
Semantic segmentation
Available
Instance segmentation
Not reported
Object detection
Not reported
Optical flow
Not reported
Depth ground truth
Not reported
Citation
Citation key and BibTeX kept at the end of the page for reference.
@inproceedings{tan2020toronto3d,
author = {Tan, Weikai and Qin, Nannan and Ma, Lingfei and Li, Ying and Du, Jing and Cai, Guorong and Yang, Ke and Li, Jonathan},
title = {Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {202--203},
url = {https://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Tan_Toronto-3D_A_Large-Scale_Mobile_LiDAR_Dataset_for_Semantic_Segmentation_of_CVPRW_2020_paper.html}
}Dataset corrections
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