Mobility LiDAR became perception training data.

Autonomous mobility point-cloud data was annotated and quality checked so perception teams could train models on roads, assets, objects and movement context.

The challenge

Point-cloud scenes include road assets, vehicles, lanes and contextual objects that need consistent interpretation.

Perception data quality depends on precise geometry and review because label errors can affect model behavior.

The approach

SBL structured annotation rules around mobility scenes, object classes and spatial consistency checks.

The team delivered reviewed datasets that could support perception training and model iteration.

The result

The client received model-ready LiDAR annotations with quality controls suited to autonomous mobility workflows.

• 3D scenes were annotated for perception model training.
• Object and asset labels became more consistent across batches.
• Quality review helped control spatial-label errors.
• The delivery model supported ongoing dataset growth.