List of Datasets

UGV

USCILab3D

Outdoor semantic 3D scene understanding

Campus robot RGB and LiDAR recordings with machine-generated semantics, merged point clouds and derived camera poses; approximately 10 million images and 6 TB.

Metadata: Complete Metadata needs review

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

Paper

Not reported

Code

Not reported

Supplementary

Not reported

Quick facts

Vehicle type
UGV
Environment
Outdoor
Data origin
Real-world
Sensor count
6
Ground truth
Available
Calibration
Not reported
Annotations
Available

Sensor overview

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

Cameras

5 reported

IMUs

Not reported

GNSS

Not reported

LiDAR

1 reported

Radar

Not reported

Pitot tube

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 LiDAR SLAM Not reported Not reported Not reported Unknown 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
Interpolated LeGO-LOAM camera poses from recorded LiDAR; not an external reference measurement.
6DoF Structure from motion Not reported Not reported Not reported Unknown 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
COLMAP SfM pose product is separate from interpolated LiDAR poses. Unified poses are described as a future version.

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
1 m by 1 m checkerboard

Calibration data

Calibration reproducibility details are not reported.

Notes

MATLAB toolbox and line/plane correspondences align camera and LiDAR frames; distribution of calibration assets is not established.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Machine-generated labels; planned human inspection and correction are not confirmed complete.

Moving objects and calibration errors can offset image-to-point-cloud projections.

Collection is confined to one university campus and daytime sessions.

Cameras

Camera 1

Model
Intel Realsense D455
Setup
Mono
Count
5
Effective cameras
5
Modality
Rgb
Resolution
1280 x 720
Rate
15 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
90 deg
VFOV
65 deg
Notes
Released RGB images; other cameras aligned to camera 1 by session-start motion detection. Depth and integrated inertial outputs are not established as released streams.

IMUs

Not reported

GNSS

Not reported

LiDAR

Velodyne HDL-32E

Model or name
Velodyne HDL-32E
Count
1
Dimensions
3
Rate
10 Hz
Channels
32
Range
Not reported

Radar

Not reported

Pitot tube

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
JSON
Class count
267
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.

Citation key
lekkala2024uscilab3d

BibTeX

@inproceedings{lekkala2024uscilab3d,
  title={USCILab3D: A Large-scale, Long-term, Semantically Annotated Outdoor Dataset},
  author={Lekkala, Kiran and Bao, Henghui and Cai, Peixu and Lim, Wei Zer and Liu, Chen and Itti, Laurent},
  booktitle={38th Conference on Neural Information Processing Systems, Track on Datasets and Benchmarks},
  year={2024}
}

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