AI Training & Data
LiDAR Annotation
3D labels your perception models can trust. Precise 3D point-cloud labeling for autonomous driving, robotics and mapping, checked twice before it reaches you.
Overview
Why it matters, and how we handle it
Perception models are only as good as the labels they learn from. Inconsistent boxes, drifting object IDs and missed edge cases cost you far more in retraining than good annotation costs up front.
Evaryn's LiDAR teams work to your guidelines, calibrate on a pilot batch first, and move every labeled sequence through independent peer review and QA, so what you receive is consistent, auditable and ready to train on.
Where clients use it
- Autonomous vehicles and ADAS
- Warehouse and delivery robotics
- Mapping, surveying and infrastructure
- Smart-city and traffic analytics
Capabilities
Exactly what we can do for you
What we label
- 3D cuboids and oriented bounding boxes for vehicles, pedestrians, cyclists and custom classes
- Semantic and instance segmentation of point clouds
- Object tracking across frames with persistent IDs
- Lane, road-edge and drivable-area marking
- Sensor-fusion labeling aligned to camera frames
How we work to your spec
- Your taxonomy, attribute lists and edge-case rules, documented before work begins
- Pilot batch to calibrate quality and agree acceptance criteria
- Export in the schema your pipeline expects, including custom formats
- Batch-level quality reports and a shared edge-case log
How we deliver
A managed process, not a hand-off
Every project follows the same five stages, so you always know where your work stands and who is responsible for it.
Brief and scope
We confirm your goals, volume, deadline and acceptance criteria, and agree a pilot batch where it makes sense.
Specialist match
Your project goes to team members approved for this category of work.
Production
Work is completed to your guidelines, with deadlines tracked and reminders built in.
Peer review
A second specialist checks accuracy and completeness and sends anything that falls short back for revision.
QA and delivery
A final quality gate signs off the deliverables before they reach you. Your feedback loops straight back to the team.
Built-in quality
Checked twice before it reaches you
Quality is part of the workflow. Nothing is delivered until it has passed both a peer review and a final QA check.
- Guideline calibration on a pilot batch before you scale
- Independent peer review of every labeled sequence
- Final QA gate before anything is delivered
- Revision loop: your feedback goes straight back to the labeling team
- Work is assigned only to named, vetted team members, and we follow the data-handling requirements you set out at scoping.
Questions
Good to know before you start
Which formats and tools do you support?
We work to your annotation guidelines and deliver in the schema your pipeline expects. Share your sensor setup and target export format at scoping and we will confirm the approach.
How do you keep labels consistent across a large dataset?
Every project starts with written guidelines and a calibration batch. Each sequence is then reviewed by a second specialist and passed through QA, and recurring disagreements are logged and resolved in the guidelines.
Can we start with a small batch?
Yes. We recommend it: send a representative sample and we will scope a pilot so you can judge quality before committing volume.
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Explore serviceReady to talk about lidar annotation?
Send us your requirements or a sample of your data or brief. We will come back with a scoped quote and a proposed pilot.