List of Datasets

Automotive

SemanticPOSS

Dynamic-rich LiDAR semantic and instance segmentation

Campus LiDAR scans with per-point semantic labels and dynamic people/rider/car instance identities.

Metadata: Complete Metadata curator reviewed Released 2020

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

Download

Not reported

Code

Not reported

Quick facts

Vehicle type
Automotive
Environment
Outdoor
Data origin
Real-world
Sensor count
1
Ground truth
Available
Calibration
Not reported
Annotations
Available

Sensor overview

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

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

1 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.
Other Manual annotation Not reported Not reported Not reported Partial · 2988 Annotated Scans; Unrecognizable Points Remain Unlabeled; Instance IDs Cover People/Rider/Car 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
Hybrid annotation workflow of range grouping/camera assistance and human point/instance correction. Derived labels are a single coherent annotation product; no independently verified trajectory truth is established.

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

Calibration and GNSS/IMU plus SLAM poses support overlapping scans for annotation; released raw calibration or independent trajectory-reference inventory is not established.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Single-campus, approximately five-minute/1.5-km capture provides limited geographic scope.

Unrecognizable points remain unlabeled; instance labels focus on people, riders and cars.

Camera imagery is described as annotation-only with future publication in this source.

Native fourteen-class labels and eleven-class evaluation must be distinguished.

The coauthor’s project page is accessible, but the lab’s referenced download host timed out during review. A verified direct data-download endpoint is not established.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

LiDAR 1

Model or name
LiDAR 1
Count
1
Dimensions
3
Rate
Not reported
Channels
40
Range
200 m

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
SemanticKITTI-compatible point clouds and uint32 point labels: lower 16 bits semantic label, upper 16 bits temporally consistent instance ID. Separate tag files support range-image generation.
Class count
[unlabeled people rider car trunk plants traffic sign pole trashcan building cone/stone fence bike ground]
Semantic segmentation Available
Instance segmentation Available
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
pan2020semanticposs

BibTeX

@misc{pan2020semanticposs,
  title = {SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances},
  author = {Yancheng Pan and Biao Gao and Jilin Mei and Sibo Geng and Chengkun Li and Huijing Zhao},
  year = {2020},
  eprint = {2002.09147v1},
  archivePrefix = {arXiv}
}

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