“We were very happy with the quality of the data that we purchased. It will assist our efforts to improve our product. The team at Unidata was very responsive to our needs, and provided the data format needed to complete our tasks. We are happy to state that Unidata delivers a high quality product in an effective and efficient manner.”
3D Annotation Services
Unidata delivers high-precision 3D annotation for AI training—from LiDAR and meshes to medical scans. We label bounding boxes, segment objects, and track motion across autonomous driving, robotics, healthcare, and construction. Our expert team ensures quality, scalability, and fast turnaround.
- 95%+ annotation accuracy
- 1,000+ domain-matched annotators
- Pilot launched within days
Data Annotation Vs Labeling Tasks
| 3D Data Annotation | 3D Data Labeling | |
|---|---|---|
| Definition | Detailed marking of three-dimensional objects, surfaces, volumes, and spatial relationships within 3D models or scenes | Assigning classification labels to entire 3D objects or simple volumetric regions |
| Work Coverage | Comprehensive spatial understanding: object boundaries in 3D space, surface normals, volumetric occupancy, scene composition | Object-level or simple region categorization without detailed geometric boundaries |
| Common Tasks | • 3D bounding cuboids • Mesh segmentation • Point cloud classification per point • Surface normal annotation • Volumetric occupancy marking • 3D keypoint detection • Scene graph generation • Object pose estimation | • 3D object classification (chair/table/car) • Scene type identification • Simple presence/absence of objects • Basic material type tagging • Indoor vs. outdoor categorization |
| Complexity Level | Very high complexity: requires 3D spatial reasoning, understanding of geometry, and multi-view perspective integration | Medium complexity: requires basic 3D understanding but without precise boundary marking |
| ML Impact | Enables: 3D object detection, scene understanding, robotics manipulation, AR/VR applications, 3D reconstruction | Enables: 3D object classification, basic scene categorization, 3D search and retrieval |
3D Data Annotation Types
Software We Use
SuperAnnotate
SuperAnnotate is an advanced platform that offers robust 3D annotation tools along with project management features. It is particularly well-suited for complex 3D data such as point clouds and LiDAR scans, providing precision and automation for efficient workflows.
Best For:
Teams needing a powerful, AI-assisted tool for managing and annotating complex 3D data in large-scale projects.
Key Features
- AI-assisted tools for 3D bounding box annotation and point cloud segmentation.
- Collaboration features for large teams, including role-based access control.
- Supports a wide range of 3D annotation types, including semantic segmentation and keypoint annotation.
- Seamless integration with popular machine learning frameworks and cloud storage solutions.
CVAT (Computer Vision Annotation Tool)
CVAT is an open-source annotation tool developed by Intel that supports a wide variety of annotation types, including 3D data. It is particularly well-regarded for its flexibility and ability to handle detailed, custom annotation tasks, making it ideal for projects that require a high level of customization.
Best For:
Developers and researchers who need a customizable, open-source solution for 3D data annotation tasks.
Key Features
- Supports 3D point cloud annotation, including 3D bounding boxes and semantic segmentation.
- Customizable interface with scripting capabilities for specialized tasks.
- Free and open-source, with strong community support and continuous updates.
- Ability to handle large datasets with detailed annotations.
Scalabel
Scalabel is an open-source, scalable platform designed for collaborative annotation of both 2D and 3D data. It supports a variety of 3D annotation types, making it suitable for projects that involve large datasets and require precise annotation tools.
Best For:
Teams and organizations needing a scalable and collaborative platform for large-scale 3D annotation projects.
Key Features
- Supports 3D bounding boxes, point cloud annotation, and object tracking in 3D space.
- Real-time collaboration tools for team-based projects.
- Scalable architecture for handling large datasets efficiently.
- Open-source, allowing for customization and integration with existing workflows.
Labelbox
Labelbox is a comprehensive data annotation platform that extends its capabilities to 3D data. It offers advanced tools for managing and annotating 3D point clouds and LiDAR data, combined with powerful project management and collaboration features.
Best For:
Enterprises and teams needing a robust, enterprise-grade solution for managing and annotating complex 3D datasets.
Key Features
- AI-powered tools for 3D point cloud segmentation and bounding box annotation.
- Supports a variety of 3D annotation types, including semantic segmentation and object tracking.
- Integrated project management tools for tracking progress and collaboration.
- API support for seamless integration with machine learning pipelines.
VGG Image Annotator (VIA) - 3D Mode
VGG Image Annotator (VIA) is a lightweight, open-source annotation tool that includes support for 3D data annotation. Its 3D mode allows for basic 3D bounding box and point cloud annotations, making it suitable for smaller projects or those requiring simple 3D annotations.
Best For:
Individuals and small teams looking for a simple, no-frills tool for basic 3D annotation tasks.
Key Features
- Supports basic 3D bounding box annotation and point cloud labeling
- Lightweight and easy to use, with no need for extensive setup.
- Open-source, allowing for modifications and customization.
- Ideal for projects with straightforward 3D annotation needs.
3D Slicer
3D Slicer is an open-source software platform for the analysis and visualization of medical imaging data, but it also includes robust 3D annotation capabilities. It is particularly strong in handling volumetric data and is widely used in medical research and clinical environments.
Best For:
Medical researchers and professionals needing advanced tools for annotating and analyzing 3D medical imaging data.
Key Features
- Supports 3D volumetric annotation, including segmentation and landmark labeling.
- Extensive tools for medical imaging data, including CT, MRI, and ultrasound.
- Open-source with a large user community and comprehensive documentation.
- Customizable with a wide range of plugins and extensions.
VoTT (Visual Object Tagging Tool)
VoTT by Microsoft is an open-source annotation tool that supports both 2D and 3D data. While primarily known for 2D annotations, it also provides capabilities for annotating 3D point clouds, making it a versatile option for teams working with mixed data types.
Best For:
Teams needing a flexible tool that can handle both 2D and 3D annotation tasks, particularly those integrating with Microsoft Azure services.
Key Features
- Supports 3D point cloud annotation, including bounding boxes and object classification.
- Integration with Azure ML and other cloud services for seamless data processing.
- User-friendly interface that simplifies the annotation process.
- Free and open-source, with active community support.
How Unidata Provide Data Labelling Process
A Clear, Controlled Workflow From Brief to Delivery
01
Kickoff Briefing and Task Setup
- You
- Share your raw data, annotation requirements, and quality standards
- Unidata
- We analyze your data, define the methodology, and assign a dedicated project lead. The right annotation type and domain-matched annotators are confirmed before anything starts.
02
Pilot & Scoping Pilot and Estimate
- You
- Review annotated samples, validate quality, and approve scope before full-scale work begins.
- Unidata
- We annotate a small representative sample and deliver a clear cost estimate broken down by complexity, hours, and validation rounds.
03
Legal & Confidential Agreement and NDA
- You
- Review and sign. Scope, quality thresholds, and deadlines are all defined in writing upfront.
- Unidata
- We prepare a full confidentiality agreement covering your data, guidelines, and any proprietary model details.
04
Technical Setup Tools and Workflow Configuration
- You
- Share existing guidelines and format requirements. No guidelines yet? We build them together.
- Unidata
- We configure the right annotation platform for your data type: Labelbox, SuperAnnotate, CVAT, or Label Studio. Workflows, label taxonomy, and quality benchmarks are set before a single label is applied.
05
Execution Annotation in Progress
- You
- Review sample batches at each milestone and share feedback with your project lead.
- Unidata
- Trained, domain-matched annotators work through your dataset. No batch moves forward without passing internal quality checks.
06
QA Human-in-the-Loop Review
- You
- Review edge cases and confirm acceptance criteria before final delivery.
- Unidata
- Every batch goes through automated validation and human review. Inter-annotator agreement (IAA) is tracked throughout. Inconsistencies are caught and resolved before the dataset moves forward.
07
Delivery Production-Ready Dataset
- You
- Receive your annotated dataset in the format you need: COCO, Pascal VOC, JSON, CoNLL, PCD, or custom. Full quality report included.
- Unidata
- Clean, validated, training-ready data delivered on schedule. Final invoice aligned to the scope agreed at Step 02.
Have questions about the process? Every project starts with a free consultation — no commitment required.
Data Annotation Challenges? Value You Get with Unidata
Real Challenges
- No annotators, tools, or workflow to process collected data
- No quality check on labeled data before it hits the pipeline
- No way to ensure two annotators label the same object consistently
- Can’t find annotators with LiDAR, medical, or financial expertise
- Scope creep and rework cycles exhaust the budget before delivery
Value with Unidata
- Project lead assigned and pilot launched within days
- Every batch validated before delivery, 95%+ accuracy via multi-stage QA
- Label consistency tracked per batch, issues caught before training fails
- 1,000+ annotators matched by domain — the right expert, every time
- Pilot-first pricing, fixed scope, zero hidden rework charges
Files Example
Working with annotation data from CVAT and JSON formats, you'll receive optimized code that seamlessly processes both file types, complete with practical examples and visual representations of your data structure.
3D Annotation Cases
Digital Tree Passport Annotation for Forest Mapping
- Forestry Monitoring & GIS
- 200,000 trees, 10 species classes
- 2 months
License Plate Annotation for Vehicle Recognition System
- 100,000 images with detailed license plate markup (bounding boxes, digits, regional symbols)
- 3 weeks
Sentiment Annotation for Brand Monitoring
- Marketing & Consumer Insights
- 12,000 text samples
- 7 weeks
Surveillance Video Annotation for Entrance Monitoring
- Surveillance & Security
- 90 minutes of video from three cameras, approximately 50-60 thousand frames
- 2 week
Other Services
Ready-Made Datasets
Get our ready-made datasets to enhance the quality of your models and improve testing
Data Collection
Collect and enhance diverse image, video, text, and audio data for your business
Data Annotation
Get accurate data labeling and annotation for your machine learning projects
LLM Training Services
Comprehensive data services for training, evaluation, and testing of LLM models across 12 industries
FAQ
What is 3D data annotation?
3D data annotation is the process of labeling three-dimensional data such as LiDAR scans, point clouds, and 3D maps to create structured training datasets. It involves annotating objects in 3D space using techniques like cuboid annotation, semantic segmentation, and object classification so ML algorithms can understand depth, distance, and spatial relationships.
Why are 3D data annotation services important for AI and machine learning?
3D data annotation services provide high-quality training data for computer vision, recognition technology, and ML models that work with LiDAR and 3D sensors. Properly annotated datasets help models understand spatial environments, improving object detection, scene analysis, and real-world decision-making.
What types of 3D annotation do you support?
We support a wide range of 3D annotation types, including cuboid annotation, 3D cuboids, semantic segmentation, cloud segmentation, and object classification. These techniques are used for identifying objects, analyzing scenes, and processing complex data from LiDAR sensors and 3D scans.
What are the risks of poor-quality 3D annotation?
Low-quality 3D annotations can lead to inaccurate training datasets and poor performance of ML models in real-world environments. Errors in labeling objects or spatial relationships can impact detection algorithms, increase retraining costs, and reduce reliability in applications like autonomous systems and robotics.
What annotation accuracy can we expect?
Our annotation services deliver 95%+ accuracy, validated daily by the Quality Control Department (QCD). Accuracy targets are defined based on your data types, LiDAR technology, and project requirements before annotation begins.
Can I order a pilot project?
Yes, Unidata offers pilot projects so teams can evaluate 3D annotation quality, workflows, and compatibility with their ML models. This helps validate outsourcing decisions before scaling to large-scale datasets.
How is our data kept secure?
All our 3D data annotation services are GDPR and CCPA compliant, and we use AWS infrastructure certified under ISO 27001 and ISO 27701. Strict access controls ensure secure handling of raw data throughout the annotation process.
How do you ensure the quality of 3D annotations? Do you use automation for validation?
We combine expert human annotators with a structured validation workflow to ensure accurate 3D annotations across complex datasets. Each project goes through multiple review stages to maintain consistency in labeling three-dimensional space. We track key metrics such as Error Rate, IAA (Inter-Annotator Agreement), and IoU (Intersection over Union), and use benchmark (“golden”) samples to evaluate performance. AI-assisted tools and annotation platforms support efficiency while maintaining high quality.
How long does it take to complete a 3D annotation project?
Timelines depend on dataset size, complexity of 3D scans, and annotation requirements. Each project is evaluated individually to provide a clear and realistic delivery schedule.
What technical support do you provide after purchasing 3D data annotation services?
Clients receive continuous support from dedicated project managers throughout the annotation process. This ensures smooth communication, quick issue resolution, and alignment with your ML project goals.
Industries
Construction & Architecture
Building model labeling, progress tracking, and design flaw identification for projects.
Healthcare
Medical scan labeling for tumor detection, surgical planning, and treatment monitoring.
Robotics
Object recognition, environment navigation, and autonomous task execution training.
Automotive
LiDAR data labeling for object recognition, spatial understanding, and safe navigation.
Retail & E-commerce
Product geometry modeling, AR experiences, and virtual try-on enhancement.
Agriculture
Crop health monitoring, pest detection, and precision farming optimization.
Security & Surveillance
Environment modeling, threat detection, and real-time monitoring improvement.
Sports & Fitness
Player tracking, biomechanics analysis, and performance optimization insights.
Why Companies Trust Unidata’s Services for ML/AI
Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.
Ready to get started?
Tell us what you need — we’ll reply within 24h with a free estimate
We partnered with Unidata for a vast collection of webcam and smartphone assets and were very satisfied. The data quality was excellent, maintaining high resolution and clarity across all formats to meet our specific technical needs. The procurement process was seamless thanks to their customer service. The team was proactive and transparent, ensuring smooth communication from start to finish. Although the investment was significant, the professional caliber of the data and the reliability of the service made it a worthwhile value.
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