3D Point Cloud Annotation Services

High-quality 3D point cloud annotation and LiDAR data labeling services for autonomous vehicles, robotics, mapping, smart mobility, object detection, and AI training datasets.

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Managed LiDAR and 3D Annotation for AI Projects

Contalents helps AI companies and computer vision teams transform raw 3D point cloud data into structured training datasets through trained annotation teams, project guidelines, QA workflows, and scalable human-in-the-loop operations.

3D Cuboid Annotation

Label vehicles, pedestrians, cyclists, objects, and obstacles using 3D cuboids for detection and perception models.

Object Tracking

Track objects across sequential LiDAR frames to support motion understanding and autonomous system training.

Semantic Segmentation

Classify points into meaningful categories such as roads, buildings, vehicles, pedestrians, lanes, and objects.

QA Workflows

Review annotated point cloud data through structured quality checks, correction loops, and reviewer feedback.

Why 3D Point Cloud Annotation Matters

3D point cloud data gives AI systems spatial understanding of the world. Accurate LiDAR annotation helps perception models detect objects, estimate distance, understand movement, classify environments, and make safer decisions in real-world conditions.

Train autonomous vehicle perception models
Improve 3D object detection accuracy
Support robotics, mapping, and smart mobility
Classify roads, lanes, buildings, and obstacles
Create reliable AI training datasets
Scale LiDAR labeling with managed QA

3D Point Cloud Annotation Services We Provide

Flexible 3D data labeling workflows for LiDAR datasets, autonomous vehicles, robotics, geospatial mapping, smart infrastructure, and computer vision projects.

3D Bounding Boxes

Annotate objects with 3D boxes to define object position, dimension, orientation, and category in point cloud scenes.

LiDAR Object Detection

Label vehicles, pedestrians, cyclists, traffic objects, road signs, barriers, and obstacles for detection models.

Point Cloud Segmentation

Segment individual points or object groups into meaningful classes for environmental understanding.

Multi-Frame Tracking

Track objects across multiple frames to support motion prediction, trajectory analysis, and temporal consistency.

Sensor Fusion Support

Support annotation workflows that combine LiDAR, camera images, video frames, and multi-sensor datasets.

Scene Classification

Classify point cloud scenes by road type, environment, traffic condition, object density, or project-specific labels.

Human-in-the-Loop LiDAR Labeling at Scale

Contalents provides managed annotation teams for complex 3D datasets where precision, consistency, reviewer alignment, and scalable QA workflows are critical.

How Our 3D Annotation Process Works

A structured workflow for accurate point cloud labeling, project calibration, reviewer alignment, and quality delivery.

1. Scope & Label Rules

We define object classes, annotation type, frame rules, scene categories, edge cases, QA criteria, and delivery format.

2. Tool & Team Setup

We prepare the annotation team, project guidelines, sample tasks, labeling tool access, and review workflow.

3. Pilot Batch

We complete a pilot batch to test instructions, align quality, identify unclear cases, and confirm labeling expectations.

4. Production Annotation

The team labels 3D datasets based on agreed classes, frame rules, tracking requirements, and delivery schedule.

5. Quality Review

Output is reviewed using sampling, frame checks, cuboid alignment review, corrections, and feedback loops.

6. Delivery & Reporting

We deliver annotated point cloud outputs based on the required tool export, data structure, or client-specific format.

Designed for Advanced AI and Mobility Projects

3D point cloud annotation projects require precision, technical understanding, consistent object rules, and careful QA. Contalents provides managed operations to help AI teams move from raw LiDAR data to usable training datasets.

Managed LiDAR annotation teams
Cuboid and segmentation QA
Support for multiple annotation tools
Scalable project capacity
Project coordination and reporting
Secure data handling workflows

Frequently Asked Questions

Common questions companies ask before starting a 3D point cloud annotation or LiDAR labeling project.

What is 3D point cloud annotation?

3D point cloud annotation is the process of labeling LiDAR or 3D sensor data so AI models can detect objects, understand spatial environments, classify scenes, and track movement.

What types of 3D annotation do you support?

Contalents supports 3D cuboids, object detection, point cloud segmentation, scene classification, multi-frame tracking, and sensor fusion annotation workflows.

Can you work with our annotation tool?

Yes. Depending on the project requirements, Contalents can work with client-provided annotation tools or agreed labeling platforms.

Do you provide quality control?

Yes. 3D annotation projects can include pilot batches, reviewer checks, sampling, cuboid alignment review, corrections, feedback loops, and project reporting.

Can you scale point cloud annotation teams?

Yes. We can scale managed annotation teams based on dataset volume, complexity, timeline, annotation type, and quality requirements.

What industries use point cloud annotation?

Point cloud annotation is used in autonomous vehicles, robotics, smart cities, mapping, infrastructure inspection, mobility systems, logistics automation, and advanced computer vision research.

Need 3D Point Cloud Annotation Services?

Tell us about your LiDAR dataset, annotation classes, tool requirements, and quality expectations. Contalents will help you build a managed point cloud labeling workflow.

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