LiDAR Annotation Services | 3D Point Cloud Labeling for AI
Annotera provides specialist LiDAR point cloud annotation for autonomous vehicle perception, robotics navigation, and geospatial AI. Our 3D annotation team delivers precise object detection, semantic segmentation, and multi-sensor fusion labeling at production scale — with three-layer QA and sub-centimeter placement accuracy.
LiDAR Annotation Types
Annotera covers the full range of 3D point cloud annotation tasks required by AV and robotics AI teams:
- 3D Cuboid / Bounding Box: Precise 3D bounding boxes with position (x, y, z), rotation (yaw, pitch, roll), and dimensions for vehicles, pedestrians, cyclists, and objects of interest
- Semantic Segmentation: Point-level classification — road surface, sidewalk, vegetation, building, dynamic objects, and sky — for HD map building and scene understanding
- Ground Plane Segmentation: Drivable surface extraction including road, shoulder, curb cuts, and off-road terrain classification
- Lane & Road Marking: Polyline and polygon annotation of lane boundaries, stop lines, crosswalks, and road edge markings
- Object Tracking: Multi-frame consistent object IDs across LiDAR sweeps for trajectory modeling and motion prediction
- Intensity Classification: Reflectivity-based material and surface classification for sensor calibration models
Supported Sensors & Formats
| Category | Details |
|---|---|
| Sensors | Velodyne (VLP-16, VLP-32, HDL-64), Ouster OS1/OS2, Luminar Iris, Hesai XT32/QT64, Livox Mid-360 |
| Input formats | .pcd, .bin (KITTI), .las, .laz, rosbag, nuScenes raw |
| Output formats | KITTI, nuScenes, Waymo TFRecord, JSON, CSV, COCO-3D, custom |
| Dataset standards | KITTI, nuScenes, Waymo Open Dataset, Lyft Level 5, Argoverse |
LiDAR + Camera Fusion Annotation
Modern AV stacks fuse LiDAR with camera, radar, and HD maps. Annotera’s fusion annotation team delivers cross-modal label consistency across all sensor modalities:
- LiDAR 3D cuboids aligned with 2D camera bounding boxes (extrinsic calibration-aware)
- Radar return integration with LiDAR object bounding boxes
- HD map overlay: lane graph, traffic element positions, semantic regions
- Consistent object IDs across LiDAR, camera, and radar frames
Industries Using Annotera LiDAR Annotation
- Autonomous vehicles: L2–L5 perception models, OEM supplier toolchains, ADAS evaluation datasets
- Robotics: Warehouse AMR navigation, outdoor robot path planning, manipulator workspace mapping
- Geospatial / mapping: Aerial LiDAR for urban planning, infrastructure inspection, forestry analysis
- Smart cities: Intersection monitoring, pedestrian flow analysis, traffic management AI
Quality & Scale
Annotera delivers LiDAR annotation at enterprise scale without sacrificing precision:
- Three-layer QA: annotator self-review → peer review → senior QA sign-off
- IAA target: ≥85% Cohen’s kappa per object category
- 350+ annotators scalable to multi-million frame projects
- 48-hour pilot: send us a sample PCD file and see labeled output before committing
Ready to label your LiDAR dataset? Get a free LiDAR annotation pilot or explore all image annotation services.
What is the cost of LiDAR annotation?
LiDAR annotation pricing depends on point cloud density, object class complexity, frame volume, and required accuracy. Annotera provides custom quotes after reviewing a sample of your data. Most LiDAR projects start with a free 48-hour pilot of 50–100 frames. Request a quote.
What is 3D cuboid annotation in LiDAR data?
3D cuboid annotation (also called 3D bounding box annotation) places a precise box around each object in a LiDAR point cloud, specifying its 3D center coordinates, width, length, height, and rotation angle (heading/yaw). This is the primary annotation format for training object detection models in AV and robotics perception systems.
How does LiDAR annotation differ from 2D image annotation?
LiDAR annotation operates on 3D point clouds (sets of x,y,z coordinates) rather than 2D pixel grids. It requires specialized annotation tools, understanding of LiDAR sensor physics, and 3D spatial reasoning. Accuracy is measured by 3D IoU (intersection over union) rather than 2D pixel overlap.
