3D Bounding Box Annotation
LiDAR point cloud labelling with tight box fitting, orientation accuracy, and truncation/occlusion handling. Schema enforced per client taxonomy. Precision-checked per batch.
Industry
Robotics &
Autonomous Vehicles
Perception systems only perform as well as the training data they're built on. Fuzu Atlas provides governed annotation at the scale and quality that safety-critical robotics and AV programs demand.
The Challenge
Why annotation quality is a safety requirement, not a preference
In AV and robotics, annotation errors directly affect system safety. A misclassified pedestrian in a training example is a failure mode waiting to emerge in production. The cost of reannotation or retraining far exceeds the cost of doing annotation correctly the first time.
Semantic & Instance Segmentation
Pixel-level object class annotation for camera feeds. Lane markings, drivable surface, pedestrians, cyclists, and custom object classes. Boundary precision tracked in QA.
LiDAR-Camera Fusion Labelling
Cross-modal annotation aligning point cloud labels with corresponding camera frames. Consistency checks across sensor modalities to prevent label mismatches.
Edge Case Dataset Construction
Targeted annotation programs for long-tail scenarios: unusual pedestrian behaviour, degraded conditions, non-standard signage, construction zones, and rare object classes.
Temporal Video Annotation
Frame-level annotation across video sequences with object ID tracking. Action and intention labelling for pedestrian and cyclist behaviour prediction.
Geographic Diversity Coverage
Fuzu Atlas's Africa-origin talent provides access to annotators with knowledge of road environments in emerging markets — cities and infrastructure types underrepresented in standard AV datasets.
Annotation quality standards for safety-critical programs
Fuzu Atlas's governance infrastructure is particularly well-suited to AV programs that require documented quality assurance for regulatory review.
Schema
Co-designed taxonomy
Object classes, attribute definitions, and edge case handling defined with your team before annotation begins.
Calibration
Precision calibration run
Small calibration batch with precision metrics reviewed jointly before full production launch.
QA
Independent quality review
QA annotators separate from production team. Authority to reject batches below quality threshold.
Audit
Full audit trail
Annotator identity, review status, quality score, and rework history per sample — available for regulatory review.
Most relevant solutions & use cases
Building a perception pipeline that needs to be reliable?
Start with a calibration sprint — schema design, precision benchmarking, and a first verified annotation batch.