Annotation Services
Human-Verified Labelling at Machine Scale
Training-ready datasets produced by domain experts — 2D, 3D, semantic, and instance-level annotations across LiDAR, camera, and radar modalities.
Talk to Our Team99.8%
Label accuracy
500M+
Frames annotated annually
30+
Object classes supported
ASIL-D
Compliance standard
Why it matters
Every autonomous system is only as good as the data it was trained on. VoxelVeritas annotation pipelines combine expert human judgement with rigorous QA workflows to produce labelled datasets that meet ASIL-D compliance standards — at the scale modern AI programmes demand.
Annotation types
Every Label Type Your AI Programme Needs
2D Bounding Box
Axis-aligned and rotated 2D bounding boxes for vehicles, pedestrians, cyclists, and road objects in camera imagery. Supports multi-class, multi-instance labelling with occlusion flags and truncation metadata.
2D Bounding Box — sample output
3D Bounding Box
Full 6-DOF cuboid annotations in LiDAR point clouds — position, dimensions, and heading angle for every object. Calibrated to camera frames for cross-modal consistency.
3D Bounding Box — sample output
Key Point Annotation
Skeletal key point labelling for pedestrian pose estimation, driver monitoring, and hand/gesture recognition. Supports 17-point COCO-style and custom automotive schemas.
Key Point Annotation — sample output
Instance Segmentation
Pixel-precise instance masks for every object class — enabling models to distinguish individual instances of the same class in dense urban scenes.
Instance Segmentation — sample output
Semantic Segmentation
Full-scene pixel classification across 30+ road-domain categories: drivable surface, lane markings, sky, vegetation, buildings, and dynamic objects.
Semantic Segmentation — sample output
Polyline & Lane Annotation
Lane boundary polylines, road edge markings, and centre-line topology for HD Map ground truth and lane-keeping system training.
Polyline & Lane Annotation — sample output