Precision LiDAR annotation powering the next generation of autonomous vehicles, robotics, and 3D perception technologies.
End-to-end expertise for every business challenge
Point Cloud Annotation
Design Lead
2023–Present
Comprehensive 3D point cloud labeling and classification across dense and sparse datasets, enriched with multi-sensor fusion inputs and consistent temporal alignment. Applied in autonomous driving, advanced mapping solutions, and intelligent robotics systems.
3D Bounding Boxes
Role
2021-2022
Accurate object positioning within three-dimensional space, supporting multiple classes with reliable coordinate precision and occlusion management. Widely used for object detection, tracking systems, and autonomous navigation technologies.
Object Tracking
Nova Technologies
2021-2022
Detailed frame-by-frame trajectory annotation ensuring multi-object coordination and persistent ID consistency across sequences. Enables vehicle tracking, motion forecasting, and in-depth behavioral analytics.
Semantic Segmentation
Role
2021-2022
Precise pixel- and point-level categorization covering roads, lanes, infrastructure, and dynamic versus static elements with hierarchical structuring. Essential for scene interpretation, drivable area detection, and environmental mapping.
Industries Served
Role
2021-2022
We support industries including autonomous vehicles with end-to-end perception pipelines, robotics for navigation and manipulation datasets, and surveying and mapping for geospatial intelligence. Our expertise also extends to construction site modeling and progress analysis, agricultural crop monitoring, and smart city initiatives focused on urban planning and infrastructure optimization.
Step 01
Discover
We learn your annotation goals, data landscape and ML objectives
Together we define the labeling schema, quality standards and success metrics
We scope the volume, timeline and resources needed
A clear roadmap is established before any work begins
Step 02
Setup
Raw data is ingested, cleaned and organized
Formats are standardized and quality assessed
Workflows and quality baselines are configured
Reference sample annotations are created for annotator alignment
Step 03
Pilot
A proof-of-concept batch is executed with trained annotators
Real-time quality monitoring and iterative feedback loops run in parallel
Accuracy is verified through multi-level validation and consensus checks
Edge cases are documented and the approach is confirmed before scaling
Step 04
Steady-State
Full-production delivery kicks off with continuous QA and optimization
Datasets are delivered organized, formatted and fully documented
Metadata, annotation statistics and audit trails are provided
Seamless handoff and integration into your ML pipelines
End-to-end expertise for every business challenge