AI Training Data Support
3D Point Cloud Annotation Services
Uniworld OS helps AI, computer vision, robotics, mapping, and autonomous-system teams transform raw LiDAR and depth-sensor data into structured, training-ready annotations. Our support covers 3D cuboids, point-level segmentation, object classification, attributes, and frame-to-frame tracking.
Managed Annotation Support
Convert Complex Spatial Data into Clear Machine-Learning Labels
A point cloud represents an environment through a large collection of three-dimensional coordinates. Before a machine-learning model can understand vehicles, pedestrians, equipment, road elements, buildings, terrain, or indoor objects, the relevant points and objects must be identified, classified, and labelled according to a defined taxonomy.
As part of our broader data and image annotation services, Uniworld OS supports 3D labelling workflows configured around your data type, annotation tool, classes, attributes, quality criteria, and output format. We can assist with sample batches, recurring production volumes, controlled review cycles, and feedback-based corrections.
The service can support raw LiDAR point clouds, depth-sensor data, sequential frames, synchronized camera inputs, and project-specific annotation schemas. Where your workflow combines 3D and 2D labelling, our teams can also support image annotation and video annotation services.
- LiDAR, depth-camera, and other spatial sensor datasets
- Project-specific object classes, attributes, and edge-case rules
- 3D cuboids, semantic segmentation, instance segmentation, and tracking labels
- Structured output prepared for your platform, schema, and delivery workflow
3D Annotation Capabilities
Point Cloud Labelling Services Aligned to Your Workflow
The scope is configured around your annotation guidelines, object taxonomy, data format, platform, review rules, and delivery requirements.
3D Cuboid Annotation
Three-dimensional boxes are placed and adjusted around vehicles, pedestrians, equipment, furniture, and other discrete objects. The work can capture object position, dimensions, orientation, visibility, and other required attributes. For two-dimensional object-detection work, see our bounding box annotation services.
Point-Level Semantic Segmentation
Selected points are assigned class labels so models can distinguish roads, surfaces, structures, vegetation, vehicles, pedestrians, equipment, and other environmental elements. Related 2D workflows are available through our semantic segmentation service.
Instance Segmentation
Individual objects within the same class are separated and labelled as distinct instances, supporting projects that require object-level identification rather than only broad semantic classes.
Object Classification and Attributes
Objects can be assigned approved class names and attributes such as object type, orientation, movement state, visibility, truncation, confidence, or other fields defined in your project instructions.
Frame-to-Frame Object Tracking
Consistent identifiers are maintained across sequential point cloud frames to support temporal analysis, movement understanding, trajectory modelling, and object persistence.
Sensor-Aligned Annotation Support
Where point clouds are reviewed alongside synchronized camera imagery, the workflow can be coordinated with 2D labels, object attributes, and visual references supplied through your annotation platform.
Use Cases
Where 3D Point Cloud Annotation Is Used
Structured 3D labels help computer-vision systems understand depth, position, surfaces, movement, and spatial relationships.
Autonomous Mobility and ADAS
Annotate vehicles, pedestrians, lanes, obstacles, traffic elements, and surrounding environments for perception and navigation models.
Robotics and Warehousing
Label people, racks, pallets, forklifts, equipment, inventory zones, and navigable spaces within indoor operational environments.
Mapping and Survey Data
Classify buildings, terrain, roads, vegetation, urban assets, and other elements captured through surveying or spatial mapping workflows.
Construction and Site Analysis
Identify structures, equipment, materials, site objects, and environmental classes for project documentation and model training.
Manufacturing and Inspection
Label machinery, components, workspaces, industrial objects, and spatial relationships for inspection and automation use cases.
Traffic and Public-Space Analysis
Annotate road users, public spaces, traffic movement, roadside assets, and infrastructure data for smart-city applications.
Engagement Workflow
How We Set Up and Run the Annotation Process
Requirement Review
Review data type, tool access, classes, attributes, volume, output format, quality rules, and delivery expectations.
Guideline Alignment
Study examples, class definitions, difficult scenarios, exception rules, and escalation requirements.
Sample Batch
Complete a controlled sample to validate interpretation, workflow, output structure, and review criteria.
Production and QA
Process assigned batches with defined validation, correction, consistency, and completeness checks.
Delivery and Feedback
Deliver approved output and apply documented feedback to subsequent production batches.
Quality Review
What We Check Before Delivery
Three-dimensional annotation requires more than placing labels. Review focuses on the geometry, class logic, attributes, consistency, and completeness defined for the project.
Why Uniworld OS
Managed Back-Office Support for Detailed Annotation Work
Project-Specific Setup
Resources and workflows are aligned with your platform, classes, attributes, file formats, and review instructions.
Structured Communication
Questions, edge cases, batch status, corrections, and feedback can be documented through an agreed process.
Scalable Production Support
Support can be planned for sample batches, recurring workloads, volume changes, and defined turnaround expectations.
Internal Service Links
Explore Related Annotation Services
Frequently Asked Questions
3D Point Cloud Annotation FAQs
What is 3D point cloud annotation?
It is the process of labelling objects, surfaces, or individual points in three-dimensional sensor data so machine-learning systems can recognise classes, positions, dimensions, movement, and spatial relationships.
Which annotation types can be included?
Depending on the project, the scope may include 3D cuboids, semantic segmentation, instance segmentation, object classification, attributes, object tracking, and LiDAR data labelling.
Can you follow our custom class taxonomy?
Yes. The workflow can be organised around your class list, attributes, naming conventions, examples, exception rules, and quality criteria.
Can you work on our annotation platform?
Tool compatibility, user access, supported formats, roles, workflow, and security requirements should be reviewed before production begins.
How are occluded or unclear objects handled?
Sparse, distant, partially visible, or uncertain objects are handled according to the approved guidelines. Uncovered edge cases can be flagged for clarification.
Do you support a pilot or sample batch?
Yes. A sample batch is recommended to confirm the classes, annotation rules, tool setup, output format, review expectations, and turnaround planning.
What information is needed for a quotation?
Share representative files, annotation type, object classes, attributes, volume, platform details, output format, quality checks, and target turnaround through the contact page.
Discuss Your 3D Point Cloud Annotation Requirements
Share a representative sample, class structure, platform details, volume, and expected turnaround so the team can review the project scope.