LiDAR Training Data for Autonomous Driving

Label vehicles, pedestrians, cyclists, and road structure in point-cloud scenes. Preserve 3D geometry, object identity, and calibrated camera context for driving perception datasets.
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LiDAR street scan with height-colored laser returns and labeled vehicle and road-user cuboids.

LiDAR Annotation for Autonomous Driving

Label road users, road structure, and object motion with consistent 3D geometry and camera context.
LiDAR street scan with height-colored laser returns and labeled vehicle and road-user cuboids.

Vehicle Cuboids

Fit oriented 3D boxes to cars, buses, and trucks. Inspect position, dimensions, and rotation with top, side, and rear views.
LiDAR point cloud with separate cuboids around a pedestrian and cyclist, alongside calibrated camera context.

Vulnerable Road Users

Label pedestrians and cyclists in their scene context. Review sparse measurements and partial visibility with calibrated camera images.
Road point cloud with labeled road points and a vertex-based polyline following the curb edge.

Road Structure and Point Labels

Trace curbs and road boundaries with 3D polylines, and paint point classes for task-defined scene regions.
Two manually edited car cuboid keyframes surround an interpolated frame on a height-colored LiDAR sequence timeline.

Temporal Object Tracks

Maintain object identity across keyframes and visibility ranges. Review interpolated cuboids and camera-guided tracking results before approval.

Built for AI Data at Scale

Annotate complex LiDAR datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
15×
Faster LiDAR data annotation

Batch workflows, camera-assisted Find Similar and Auto-Tracking, and interpolation reduce repetitive manual LiDAR labeling.

85%
Automated LiDAR annotation

On average, 85% of LiDAR labels are pre-labeled automatically, then reviewed and refined by humans.

15×
Lower Training Data Costs

The cost per accepted label can be up to 15× lower as curation, annotation, and QA are automated.

Annotation Methods for Autonomous Driving

Combine road-user cuboids, scene structure, and temporal tracks for driving perception models.
A height-colored LiDAR road scene and three synchronized camera views show the same silver car with a shared purple cuboid.

3D Cuboids with Camera Context

Inspect road-user cuboids in the point cloud and linked calibrated camera views. Use the image evidence to resolve class and boundary questions while keeping the 3D label authoritative.

Keyframes and Visibility Review

Refine cuboid keyframes where motion or visibility changes. Review interpolation across neighboring frames and apply the project’s rules for occlusion consistently.

Three height-colored LiDAR frames of Car 01 align with object keyframes, visibility and motion rows on a shared annotation timeline.

Autonomous Driving LiDAR Annotation FAQs

What is LiDAR annotation for autonomous driving?

It is the labeling of objects and spatial structure in recorded point-cloud scenes to prepare driving-perception training data. Labels can describe 3D object extents, point classes, road boundaries, and changes across scene frames.

Which road users and scene elements can I label?

Define classes for vehicles, pedestrians, cyclists, and the road elements relevant to your model. Use 3D cuboids for object extents, polylines for boundaries, and point segmentation for class-labeled regions.

Can I review LiDAR together with camera images?

Yes. Scenes can include multiple point-cloud sensors and camera images. Supply the required calibration and poses so projected labels and linked camera boxes are placed consistently across the available views.

How does automatic tracking work for 3D cuboids?

Camera-guided tracking follows projected object boxes through a calibrated camera sequence, then fits 3D cuboids to the LiDAR points. Review the generated tracks and refine keyframes while preserving manual corrections.

How do keyframes and interpolation work?

Cuboid tracks preserve object identity, geometry, keyframes, and visibility ranges. Interpolation fills position, dimensions, and rotation between keyframes. Annotators review the results against the point cloud and correct ambiguous motion or visibility changes.

Which LiDAR input formats are supported?

Unitlab supports PCD, PLY, and BIN point-cloud data, including ZIP and folder scene uploads. Scene manifests can describe sensors, frames, camera calibration, and poses. Check the format and scene structure before importing a driving dataset.

How are driving-perception annotations reviewed?

Use shared instructions, required properties, expert review, Consensus for standalone LiDAR scenes, and Quality Gate benchmarks. Inspect geometry and identity across frames, then resolve issues before approving the labels.

Can I curate scenes before annotation?

Yes. Organize scenes with folders, tags, metadata, and available filters. Select representative routes, conditions, and difficult cases, then record the selection in a dataset version for annotation and review.

Can I export reviewed 3D annotations?

Export reviewed LiDAR annotations in Unitlab Unified Export Format (UUEF), preserving supported geometry, tracks, scene metadata, calibration, and point-label arrays. Validate class conventions and coordinate handling with your training code before use.

Related resources:LiDAR annotationLiDAR quality assuranceTraffic video annotationData curationDataset managementLiDAR documentation
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