Expert annotation of LiDAR sensor data for autonomous vehicles, robotics, and HD mapping. We deliver 3D cuboid, polyline, ground plane, and semantic segmentation of point clouds — with the precision your perception models demand.
LiDAR point clouds are the primary sensor for autonomous vehicle perception. Every point in the cloud represents a 3D measurement — and your model needs those points correctly labelled to understand the world. We annotate vehicles, pedestrians, cyclists, road infrastructure, and free space with precise 3D cuboids, polylines, and ground planes.
Beyond autonomous vehicles, we support robotics workspace mapping, infrastructure inspection, and HD map creation. Our annotators are trained on leading LiDAR platforms including Velodyne, Hesai, and Luminar sensor formats.
We work with raw LiDAR frames and fused camera+LiDAR data, supporting PCD, PLY, BIN, and rosbag input formats. Output in KITTI, nuScenes, Waymo Open Dataset, or custom JSON schemas.
3D object detection and tracking for self-driving car perception stacks. We annotate vehicles, pedestrians, cyclists, and road infrastructure with precise cuboids and tracking IDs across sequential frames.
Workspace mapping and manipulation zone annotation for industrial and warehouse robots. We label objects, surfaces, obstacles, and navigable space in 3D point clouds for robot perception systems.
Road marking, lane boundary, and infrastructure annotation for high-definition maps. We trace road edges, lane dividers, traffic signs, and static objects with centimetre-level precision for autonomous navigation.
Building, bridge, and pipeline 3D scan annotation for structural assessment AI. We identify defects, deformations, and structural components in dense point cloud scans from terrestrial and mobile LiDAR systems.
Aerial point cloud annotation for survey, inspection, and mapping AI applications. We segment terrain, buildings, vegetation, and infrastructure from airborne LiDAR and photogrammetry point clouds.
Tree canopy, terrain classification, and biomass estimation from airborne LiDAR. We segment individual trees, canopy layers, and ground surfaces for forestry management and environmental monitoring AI.
We review your LiDAR sensor specs, scanning pattern, and fusion setup to tailor our annotation approach. Object taxonomy, cuboid dimensions, and tracking rules are defined before any annotation begins.
A representative set of frames annotated and reviewed against your 3D object taxonomy. IoU scores and cuboid orientation accuracy are measured to calibrate annotator performance before production.
High-throughput 3D annotation with per-frame object count QA and track consistency checks. Teams are organised by domain — AV specialists for driving scenes, robotics annotators for indoor environments.
IoU-based quality checks, cuboid orientation validation, and delivery in KITTI, nuScenes, Waymo Open Dataset, or any custom format your training pipeline requires.
Tight cuboids with correct orientation, dimensions, and class — every frame. Our annotators are trained to handle occlusion, sparse points, and edge cases that affect 3D perception model accuracy.
Teams trained on Velodyne, Hesai, Luminar, and Ouster LiDAR formats. We understand point density, scan patterns, and sensor-specific artifacts that affect annotation quality.
Ramp from pilot to full-fleet LiDAR annotation in days. Our managed team structure can handle thousands of frames per day with consistent annotation quality and tracking coherence.
Proprietary sensor data and vehicle footage protected under ISO 27001. All LiDAR data is processed in secure, access-controlled environments with full audit trails and encrypted transfer.
Direct delivery to your training pipeline via API or secure file transfer. We support all major 3D annotation formats and can adapt to custom schema requirements with minimal lead time.
3D annotation expertise at African talent rates — 40-60% below US/EU providers. You get the same precision and throughput as top-tier annotation houses at a fraction of the cost.
Send us a sample dataset and we'll return a pilot annotation within 48 hours.