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3D Point Cloud Annotation Services

Label LiDAR and depth data for perception models that need to understand the world in three dimensions. Smart Annotahub delivers 3D cuboids, point-level segmentation, and sensor-fusion labels you can train on.

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Types of 3D Point Cloud Annotation We Offer

01

3D Cuboids

Oriented bounding boxes with accurate heading, dimensions, and attributes for every object.

02

3D Semantic Segmentation

Point-level classes for ground, vegetation, buildings, road, and custom categories.

03

Object Tracking in 3D

Persistent IDs across LiDAR sweeps for motion forecasting and tracking models.

04

Sensor Fusion (2D + 3D)

Linked labels across camera images and point clouds using your calibration.

05

Lane and Road Marking

Polylines and 3D splines for lanes, curbs, and road edges.

06

Indoor and Mapping Scans

Rooms, furniture, and structural elements in indoor and terrestrial scans.

How We Deliver 3D Point Cloud Annotation

From your first sample to production volume, every project follows the same six-step path, with no commitment until the pilot proves the quality.

  1. Step 01

    NDA and Discovery

    We sign an NDA first, then clarify your goals, taxonomy, edge cases, and target accuracy for the point cloud data.

  2. Step 02

    Free Pilot

    We annotate a sample of your point cloud data at no cost, so you can judge quality, speed, and edge-case handling first-hand.

  3. Step 03

    Proposal and Agreement

    Based on your pilot feedback, we finalize scope, timeline, pricing, and the Service Level Agreement.

  4. Step 04

    Team Setup and Training

    We assemble a dedicated team, train it on your guidelines, and agree on communication channels and progress tracking.

  5. Step 05

    Production Labeling

    Our team runs 3D annotation to the agreed plan, with throughput and accuracy KPIs tracked for every annotator.

  6. Step 06

    QA and Delivery

    Your labeled point clouds pass multi-stage quality review before delivery. Your feedback feeds straight back into the guidelines.

3D Point Cloud Annotation for Your Industry

3D labeling needs spatial reasoning and strict QA. Our annotators are trained specifically on point cloud tooling.

01

Autonomous Driving

Vehicles, pedestrians, cyclists, and static objects in LiDAR sweeps.

02

Robotics and Physical AI

Grasping, obstacle avoidance, and warehouse navigation.

03

Construction and BIM

Structural element segmentation for progress tracking.

04

Energy and Utilities

Power lines, poles, and vegetation encroachment from aerial LiDAR.

05

Mapping

HD map features and infrastructure inventories.

Tools we work in

Your platform or ours. We adapt to existing pipelines, review stages, and export schemas.

Why AI Teams Choose Smart Annotahub

Handle Complex Datasets

Get consistent annotation for detailed taxonomies, edge cases, and challenging data.

Build Quality Into Every Step

We combine onboarding, clear and evolving guidelines, quality assurance, and continuous feedback.

Flexible and Scale On Your Terms

Adjust team capacity, project size, and delivery model as your needs change, with no setup fees or long-term lock-ins.

Integrate From Day One

Align on goals, workflows, and expectations with a team that fits into your process.

Work with Annotation Experts

Our team includes former annotators who understand annotation complexity, quality standards, and high-volume delivery.

Start with a free 3D point cloud pilot

Tell us about your data and requirements. We'll return an annotated sample with a precise quote, usually within a few working days.



    Reviews

    From our Clients

    ★★★★★

    “Their flexibility and ability to move fast impress us.”

    We chose Smart Annotahub for its strong value, recommendation, and shared company values. Their 10-person team delivered accurate data annotation with a flexible, collaborative approach. They responded quickly, went the extra mile to meet deadlines, and kept the project on track. We’ve been very pleased with the experience and have no improvements to suggest at this time.

    Tatsuya Ishihara
    Project Manager
    ★★★★★

    “They’re always willing to adapt quickly to our needs and help keep the project on track.”

    The team is highly responsive and flexible, quickly adapting to our needs to keep the project on track. Clear, detailed annotation guidelines help them deliver accurate results faster.

    Park Ji-Ho
    Director
    ★★★★★

    “Communication with the team is smooth, clear, and effortless.”

    We chose Smart Annotahub for its expertise, openness to new ideas, and strong interest in autonomous vehicles. Their team provides consistent cuboid and polygon annotation for our growing image dataset, with responsive communication, attentive project management, and reliable quality assurance. We’re very pleased with the collaboration and look forward to continuing our work together.

    Matthew Milner
    Director of Engineering

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    3D Point Cloud Annotation FAQs

    Which is 3D Point Cloud Annotation?

    3D Point Cloud Annotation is the process of labeling and categorizing data points inside a three-dimensional spatial dataset. Usually captured by LiDAR, radar, or depth cameras so that artificial intelligence and machine learning models can recognize objects, measure distances, and understand physical environments.

    Can you annotate camera and LiDAR together?

    Yes. With your calibration files we link 2D and 3D labels so objects share IDs across sensors.

    How do you check cuboid accuracy?

    Reviewers check fit, heading, and dimensions from multiple views, and flag inconsistencies across consecutive frames.

    Do you handle sparse or distant points?

    Yes. Guidelines define minimum point counts and occlusion rules so sparse objects are labeled consistently.