List of Datasets

Automotive

Argoverse 3D Tracking

Map-aware 3D detection and tracking

Human-annotated driving logs with ring/stereo cameras, LiDAR, fused ego poses and lane/ground/driveable-area maps.

Metadata: Complete Metadata curator reviewed Released 2019

Primary places to inspect, download, read about, or reproduce the dataset.

Supplementary

Not reported

Quick facts

Vehicle type
Automotive
Environment
Outdoor
Data origin
Real-world
Sensor count
11
Ground truth
Available
Calibration
Parameters Only
Annotations
Available

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

9 reported

IMUs

Not reported

GNSS

Not reported

LiDAR

2 reported

Radar

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
6DoF Other Not reported Not reported Not reported Full Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Hybrid
Reference notes
GPS-based and sensor-based localization are fused. Exact estimator and achieved errors are not established; no raw GNSS/IMU inputs are inferred.
6DoF Manual annotation Not reported Not reported Not reported Partial · Human-Annotated Tracking Objects in the Mapped ROI and Visible in LiDAR Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Derived
Reference notes
Cuboid poses/dimensions and identities follow LiDAR, with manual QA. SE(3) annotation coordinates support the 6-DoF pose mapping; actual permitted object tilt should be confirmed.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Parameters Only
Processed parameters
Available
Raw calibration data
Not reported
Calibration targets
Not reported

Parameters only

Processed calibration parameters are reported, but raw calibration data is not reported.

Notes

Per-log intrinsic and extrinsic information supplied for LiDAR and all nine cameras; raw calibration sequences not established.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Annotations are restricted to mapped driveable-area ROI and visible LiDAR objects.

Faces and license plates are blurred.

The released lane map simplifies the fleet operational map; no universal map/pose accuracy bound is stated.

This entry describes Argoverse 1, separate from Argoverse 2. Paper-era scope is retained; later annotation/derived-product inventories require separate review.

Cameras

Camera 1

Model
Not reported
Setup
Multi Camera
Count
7
Effective cameras
7
Modality
Rgb
Resolution
1920 x 1200
Rate
30 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Seven overlapping ring views cover 360 degrees; model/shutter not stated. Faces and plates are blurred.

Camera 2

Model
Not reported
Setup
Stereo
Count
1
Effective cameras
2
Modality
Rgb
Resolution
2056 x 2464
Rate
5 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Printed dimensions are 2056 × 2464; axis ordering needs confirmation. Baseline 0.2986 m. One pair means two imagers.

IMUs

Not reported

GNSS

Not reported

LiDAR

LiDAR 1

Model or name
LiDAR 1
Count
2
Dimensions
3
Rate
10 Hz
Channels
32
Range
200 m

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
3D cuboids with object identities and ego-frame poses; exact serialization is not specified here.
Class count
[animal bicycle bicyclist bus emergency vehicle large vehicle moped motorcycle motorcyclist on road obstacle other mover pedestrian stroller trailer vehicle]
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Available
Optical flow Not reported
Depth ground truth Not reported

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
chang2019argoverse

BibTeX

@misc{chang2019argoverse,
  title = {Argoverse: 3D Tracking and Forecasting with Rich Maps},
  author = {Ming-Fang Chang and John Lambert and Patsorn Sangkloy and Jagjeet Singh and Sławomir Bąk and Andrew Hartnett and De Wang and Peter Carr and Simon Lucey and Deva Ramanan and James Hays},
  year = {2019},
  eprint = {1911.02620v1},
  archivePrefix = {arXiv}
}

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