LiDAR & 3D Annotation Services for Autonomous Systems
Build safer autonomous systems with precise 3D point cloud annotation from Australia's leading spatial data labeling experts.
Why LiDAR & 3D Annotation Quality Matters
Autonomous vehicles, robotics, and AR/VR systems depend on accurately labeled 3D spatial data. Poorly annotated point clouds, inconsistent 3D bounding boxes, and missed object occlusions lead to dangerous perception failures. AI Taggers delivers enterprise-grade LiDAR and 3D annotation that ensures your autonomous systems understand complex spatial environments with precision.
Trusted by autonomous vehicle companies, robotics teams, and AR/VR developers to annotate millions of 3D frames with spatial accuracy and temporal consistency.
Our LiDAR & 3D Annotation Capabilities
3D Bounding Box Annotation
Create precise cuboid annotations around objects in 3D space with accurate position, dimensions, orientation, and rotation. Essential for autonomous vehicles, warehouse robotics, and drone navigation.
Point Cloud Segmentation
Classify individual points or point clusters into semantic categories like road, sidewalk, vehicle, pedestrian, vegetation, and buildings. Pixel-level accuracy in 3D space for advanced scene understanding.
3D Object Tracking
Track objects across LiDAR sequences with consistent IDs through time. Maintain tracking integrity through occlusions, sensor gaps, and object interactions. Critical for predicting trajectories.
Sensor Fusion Annotation
Synchronize and annotate data from multiple sensors including LiDAR, camera, radar, and IMU. Create unified annotations across modalities for robust perception systems.
Semantic Scene Segmentation
Label entire 3D scenes with semantic information about terrain, obstacles, free space, and environmental features. Used for path planning and spatial understanding in robotics.
Instance Segmentation
Distinguish individual object instances within the same class in 3D space. Separate multiple vehicles, pedestrians, or obstacles while maintaining their unique identities.
Lane & Road Marking Annotation
Annotate lane boundaries, road edges, crosswalks, stop lines, and road markings in 3D point clouds for autonomous driving HD map creation and lane-keeping systems.
3D Pose Estimation
Estimate 6DOF (degrees of freedom) poses of objects including position and orientation for robotic manipulation, AR object placement, and autonomous grasping applications.
Mesh & Surface Reconstruction Annotation
Label 3D meshes, surfaces, and reconstructed models for architectural analysis, industrial inspection, and digital twin applications.
Volumetric Annotation
Annotate full 3D volumes for medical imaging (CT, MRI), industrial scanning, and spatial analysis applications requiring complete volumetric understanding.
Spatial Precision & Temporal Consistency
LiDAR annotation requires expert understanding of 3D geometry and spatial relationships.
Millimeter-level accuracy
Precise cuboid fitting that captures true object dimensions and orientations in 3D space.
Occlusion handling
Expert annotation of partially visible objects, maintaining bounding box accuracy even when objects are obscured by other elements.
Cross-frame consistency
Maintain identical object IDs and smooth tracking across LiDAR sequences, even through sensor gaps and occlusions.
Multi-sensor alignment
Perfect synchronization between LiDAR point clouds and camera images for sensor fusion annotation projects.
Dense point cloud expertise
Handle high-density point clouds (1+ million points per frame) with efficient annotation workflows.
Sparse data annotation
Accurately label objects in sparse or long-range LiDAR data where point density is minimal.
Australian-Led Quality Standards
Unlike offshore 3D labeling vendors, AI Taggers operates with Australian-led quality assurance for spatial data.
Multi-stage verification process
Every LiDAR frame passes through annotator → 3D reviewer → spatial QA auditor checkpoints before delivery.
100% human-verified annotations
Real experts validate cuboid dimensions, object orientations, tracking consistency, and spatial relationships.
Geometric accuracy validation
Systematic checks for cuboid fitting errors, rotation inconsistencies, and dimensional inaccuracies.
Temporal coherence testing
Frame-by-frame review ensures tracking IDs remain consistent and object movements are physically plausible.
Edge case expertise
Our QA teams actively flag challenging scenarios like extreme occlusions, sensor artifacts, distant objects, and ambiguous point clusters.
Scalability for Autonomous AI Projects
Start with 100-500 LiDAR frames to validate our process, then scale to millions of frames without quality degradation.
LiDAR frames annotated
Points per frame capacity
Global annotation teams
Industries We Serve
Autonomous Vehicles
Vehicle detection, pedestrian tracking, cyclist identification, traffic infrastructure annotation, and obstacle detection across highway, urban, and parking scenarios.
Robotics & Industrial Automation
Warehouse robot navigation, object picking and placement, collision avoidance, and environment mapping for mobile robots and manipulators.
Drones & Aerial Systems
Terrain mapping, obstacle detection, infrastructure inspection, and navigation annotation for UAV and aerial autonomy systems.
Smart Cities & Infrastructure
3D city mapping, infrastructure asset management, construction monitoring, and urban planning spatial data annotation.
Agriculture
Crop monitoring, precision agriculture, plant counting, yield estimation, and autonomous farming equipment navigation from 3D sensor data.
Construction & Mining
Equipment tracking, terrain modeling, volume calculation, progress monitoring, and autonomous heavy machinery systems.
AR/VR & Metaverse
3D scene reconstruction, spatial mapping, object placement, and environment understanding for augmented and virtual reality applications.
Security & Surveillance
Perimeter monitoring, intrusion detection, crowd analysis, and facility security using 3D spatial awareness systems.
Why Autonomous AI Teams Choose AI Taggers
3D annotation expertise
Specialized annotators trained in spatial geometry, coordinate systems, sensor characteristics, and autonomous system requirements.
Annotation guideline development
We collaborate with your team to create comprehensive 3D annotation guidelines including cuboid fitting standards and tracking protocols.
Sensor fusion capability
Synchronized annotation across LiDAR, camera, radar, and IMU data streams with perfect temporal and spatial alignment.
Format flexibility
Deliver in KITTI, nuScenes, Waymo Open Dataset, PCD, LAS, PLY, or your custom 3D format requirements.
Secure & compliant workflows
Australian data oversight, NDAs, secure annotation environments, and encrypted data transfer for proprietary data.
LiDAR & 3D Data Types We Support
Our LiDAR Annotation Process
Consultation & Setup
We review your LiDAR data, sensor specifications, annotation requirements, and use cases. Our team develops detailed 3D annotation guidelines with spatial accuracy standards and edge case handling.
Calibration & Pilot Batch
Annotate 50-100 representative frames as a quality test. You review cuboid accuracy, tracking consistency, and annotation standards. We calibrate our workflows based on your feedback.
Full-Scale Production
Distributed 3D annotation teams begin labeling with real-time spatial QA monitoring. Weekly quality reports track geometric accuracy, tracking performance, and annotation velocity.
Delivery & Iteration
Receive annotations in your preferred format with object IDs, coordinates, dimensions, orientations, and metadata. We incorporate feedback and continuously improve as your system evolves.
3D Annotation Pricing Models
Per-frame pricing
Standard pricing based on point cloud complexity and object density per frame.
Per-object pricing
Cost-effective for sparse scenes with low object counts across many frames.
Temporal tracking premium
Additional rates for maintaining object tracking across sequences with consistent IDs.
Sensor fusion premium
Additional rates for synchronized multi-sensor annotation requiring cross-modal alignment.
Quality Metrics We Track
Geometric Accuracy
- Cuboid dimension precision (cm-level)
- Orientation angle accuracy (degree-level)
- Position accuracy (centimeter-level)
Temporal Consistency
- ID switch rate per 1000 frames
- Tracking fragmentation rate
- Occlusion handling accuracy
Annotation Coverage
- Object detection rate (recall)
- False positive rate (precision)
- Class confusion matrix
Production Metrics
- Frames per hour per annotator
- Average objects per frame
- QA pass rate
Real Results From Autonomous AI Teams
"AI Taggers delivered the most spatially accurate 3D annotations we've tested—their cuboid fitting and occlusion handling exceeded our internal team's quality."
Perception Lead
Autonomous Vehicle Company
"The temporal tracking consistency across our LiDAR sequences was flawless, even through challenging urban intersections with 50+ objects."
Robotics Engineer
Warehouse Automation Startup
Get Started With Expert LiDAR & 3D Annotation
Whether you're building autonomous vehicles, training robotic perception systems, or developing AR/VR applications, AI Taggers delivers the 3D annotation quality your spatial AI needs.
Questions about LiDAR & 3D annotation?
What sensor configuration are you using?
How many frames need annotation?
What object classes require labeling?
Do you need temporal tracking across sequences?
Our team responds within 24 hours with a tailored solution for your autonomous AI project.