AI and ML Network Services
3D Point Cloud Annotation Services
Professional 3D point cloud and LiDAR annotation services for autonomous driving, robotics, and warehouse automation. Cuboid annotation, object tracking, and sensor fusion labeling with 99%+ accuracy.
AI and ML Network provides professional 3D point cloud and LiDAR annotation services for teams building autonomous driving perception, robotics, and warehouse automation AI systems.
3D Annotation Types
Cuboid Annotation
3D bounding box placement in point cloud data, defining:
- Position — x, y, z coordinates in 3D space
- Dimensions — length, width, height of the cuboid
- Rotation — yaw, pitch, roll orientation angles
- Class — object type classification
3D Object Tracking
Multi-frame 3D tracking with consistent object IDs across LiDAR sequences. Essential for tracking vehicles, pedestrians, and cyclists through scenes for autonomous driving model training.
Sensor Fusion Annotation
Combined labeling across camera RGB data and LiDAR point clouds, ensuring annotation consistency between 2D image labels and 3D point cloud labels for fusion-based perception systems.
Use Cases
- Autonomous vehicles — vehicle, pedestrian, cyclist, and traffic object detection and tracking
- Robotics — object detection for robotic grasping and manipulation
- Warehouse automation — pallet, package, and equipment detection
- Drone perception — 3D mapping and object identification from aerial LiDAR
- Construction — equipment and safety monitoring from 3D sensor data
Platform Experience
We have hands-on experience with CVAT’s 3D annotation interface for cuboid placement on point cloud data. Our team handles the specialized spatial understanding required for accurate 3D annotation.
If you need 3D annotation for your AI project, go to the Start Project page and send your requirements.
Frequently Asked Questions
What is 3D point cloud annotation? +
3D point cloud annotation involves labeling objects in three-dimensional space captured by LiDAR sensors. Annotators place 3D cuboids around objects, defining position (x, y, z), dimensions (length, width, height), and rotation angle. This enables AI models to understand object location, size, and orientation in 3D space.
What industries use 3D annotation? +
3D point cloud annotation is primarily used in autonomous driving (labeling vehicles, pedestrians, cyclists in LiDAR data), robotics (object detection for robotic grasping), warehouse automation (pallet and package detection), drone perception (3D object mapping), and sensor fusion systems combining camera and LiDAR data.
How long does 3D annotation take compared to 2D? +
3D annotation typically takes 3-5x more time per instance compared to 2D bounding box annotation. Annotators need to understand 3D spatial geometry, maintain consistent cuboid orientation across frames, and work with specialized point cloud visualization tools.
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