3D Annotation Services for LiDAR and Point Cloud Data
3D Annotation Services
DataVLab provides 3D annotation services for LiDAR, point clouds, depth maps, and multimodal sensor data used in autonomous driving, robotics, industrial automation, and geospatial AI. We deliver high-accuracy 3D bounding boxes, cuboids, point cloud segmentation, drivable area labeling, and temporal object tracking with multi-stage QA, calibration checks, and project-specific guidelines. Whether you need a pilot dataset or large-scale production labeling, our team supports consistent, scalable workflows for advanced perception models.
High-accuracy 3D bounding boxes, cuboids, and point cloud segmentation for perception AI.
Specialized 3D labeling workflows for LiDAR, depth maps, and multimodal sensor fusion.
Geometric, temporal, and class-level QA for reliable 3D annotation at scale.
3D annotation is the process of labeling LiDAR, point cloud, and depth data so computer vision and perception models can detect, segment, localize, and track objects in 3D space. It is a core requirement for autonomous driving, robotics, mapping, and smart infrastructure systems. DataVLab provides 3D data annotation services delivered by trained specialists who understand point cloud geometry, occlusion handling, sensor noise, and multimodal alignment.
We annotate 3D bounding boxes and cuboids, point cloud semantic and instance segmentation, ground plane labels, drivable areas, lane-related surfaces, and temporal object tracks across sequences. Our teams follow geometric rules for rotation, orientation, center alignment, and scale while handling occlusions, sparse regions, partial captures, and varying point densities. We also support multimodal workflows that align LiDAR with camera and radar data.
Our 3D annotation services support autonomous vehicles, ADAS, delivery robots, warehouse automation, mobile mapping systems, smart city analytics, industrial inspection, construction monitoring, and geospatial intelligence. We work with LiDAR point clouds, depth maps, and sensor-fusion datasets, and can adapt class ontologies and labeling rules to your model requirements.
3D annotation quality depends on geometric precision and consistent review. Our workflows include calibration checks, task sampling, multi-step QA, class audits, overlap checks, and targeted review of difficult frames to improve consistency across annotators and sequences.
For sensitive projects, DataVLab supports secure delivery workflows, including GDPR-aligned processing and EU-only annotation options where required.
3D Annotation Capabilities for LiDAR and Point Cloud Projects
From cuboids and segmentation to tracking and sensor fusion, DataVLab supports 3D perception teams with trained annotators, project-specific guidelines, and structured QA workflows.
3D Bounding Boxes and Cuboids
Geometrically accurate cuboids for 3D object detection
We annotate vehicles, pedestrians, cyclists, machinery, obstacles, and infrastructure with 3D bounding boxes and cuboids that follow project rules for rotation, dimensions, center alignment, and tightness.
Point Cloud Segmentation
Semantic and instance segmentation for sparse and dense point clouds
We segment roads, vegetation, buildings, warehouse assets, industrial equipment, and custom classes with depth-aware labeling and consistent class boundaries for training and validation datasets.
Drivable Area and Ground Plane Annotation
Surface and navigable-space labels for planning and mobility models
We annotate drivable zones, walkable regions, curbs, ramps, lane-adjacent surfaces, and ground planes to support autonomous mobility, delivery robotics, and navigation systems.
3D Tracking Across Temporal Sequences
Consistent object identities across time and motion
We label and review object tracks across sequential frames to maintain identity continuity, reduce drift, and improve temporal consistency in dynamic scenes with occlusion and motion blur.
Multimodal Sensor Fusion Annotation
Aligned labels across LiDAR, radar, and camera streams
We annotate synchronized multimodal datasets by aligning image and 3D labels according to calibration rules, sensor timing, and ontology guidelines for robust perception model training.
3D Annotation Quality Control
Structured QA for geometric precision and label consistency
Our QA process includes class audits, cuboid tightness checks, rotation and orientation review, overlap validation, sequence-level consistency checks, and targeted corrections for difficult frames.
Discover How Our Process Works
Defining Project
Sampling & Calibration
Annotation
Review & Assurance
Delivery
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We provide high-quality annotation services to improve your AI's performances
Custom service offering
Up to 10x Faster
Accelerate your AI training with high-speed annotation workflows that outperform traditional processes.
AI-Assisted
Seamless integration of manual expertise and automated precision for superior annotation quality.
Advanced QA
Tailor-made quality control protocols to ensure error-free annotations on a per-project basis.
Highly-specialized
Work with industry-trained annotators who bring domain-specific knowledge to every dataset.
Ethical Outsourcing
Fair working conditions and transparent processes to ensure responsible and high-quality data labeling.
Proven Expertise
A track record of success across multiple industries, delivering reliable and effective AI training data.
Scalable Solutions
Tailored workflows designed to scale with your project’s needs, from small datasets to enterprise-level AI models.
Global Team
A worldwide network of skilled annotators and AI specialists dedicated to precision and excellence.
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