Empower your machine learning models with high-precision point cloud annotation and 3D sensor fusion data labeling. We deliver ground-truth datasets for autonomous driving, robotics, and smart infrastructure.
A complete explanation of how LiDAR data works and why annotation is the critical foundation of any autonomous AI system
LiDAR (Light Detection and Ranging) works by emitting thousands of laser pulses per second and measuring how long each pulse takes to return after bouncing off objects. The result is a precise, three-dimensional map of the surrounding environment — called a point cloud.
Unlike cameras that capture flat 2D images, LiDAR captures the real world in full 3D — recording exact X, Y, and Z coordinates, depth, height, and reflectivity for every measured point. This makes it the most spatially accurate sensor available for AI training.
3D LiDAR annotation is the process of labeling objects, surfaces, and regions within point cloud data so AI models can identify and understand them. Trained annotators place 3D bounding boxes, cuboids, and segmentation masks around every relevant object across thousands of frames.
Without annotated LiDAR data, AI systems have no way to distinguish a pedestrian from a traffic sign — or a parked car from a moving vehicle. Annotation is the bridge between raw sensor data and intelligent AI perception.
// LiDAR Point Cloud - Frame 0042
Object_01: Vehicle
X: 14.32m Y: 2.18m Z: 0.91m
L: 4.5m W: 1.9m H: 1.4m
Heading: 87.4°
Object_02: Pedestrian
X: 6.71m Y: -1.04m Z: 0.0m
L: 0.8m W: 0.7m H: 1.75m
Object_03: Cyclist
X: 22.11m Y: 3.57m Z: 0.2m
Confidence: 99.2%
3D LiDAR annotation operates in a completely different dimension — literally. Here's what makes it uniquely complex and critical.
Every annotation exists in full X, Y, Z coordinates — capturing not just where an object is, but its exact height, width, depth, and orientation in space.
LiDAR sensors work in rain, fog, and darkness where cameras fail — making annotated LiDAR data essential for reliable real-world AI performance.
A single LiDAR scan contains millions of individual data points. Annotators must accurately place 3D boxes across every relevant object in dense point clouds.
Objects must be consistently labeled across sequential frames — maintaining unique IDs and tracking trajectories as objects move through 3D space over time.
| Feature | 2D Image Annotation | 3D LiDAR Annotation |
|---|---|---|
| Dimensions | X, Y (pixels) | X, Y, Z (real-world meters) |
| Depth Information | X Not captured | Precise depth measurement |
| Object Volume | X 2D silhouette only | Exact 3D dimensions |
| Works in Low Light | X Camera dependent | All-conditions sensor |
End-to-end point cloud and LiDAR labeling solutions for every AI and autonomous systems use case
Our annotators place precise 3D cuboids around every object in your point cloud — capturing exact length, width, height, and heading angle. This is the most fundamental annotation type for object detection in autonomous driving and robotics.
We assign class labels to individual points or clusters within your LiDAR point clouds — enabling AI models to distinguish roads, buildings, vegetation, and dynamic objects at the point level for complete scene understanding.
We synchronize and co-annotate LiDAR point cloud data with RGB camera imagery to produce multi-modal training datasets. Fusion annotation bridges 3D spatial precision with rich visual texture — the gold standard for autonomous perception.
Precise labeling of lane boundaries, road edges, crosswalks, stop lines, and road surface features within LiDAR data — critical for path planning and HD map creation in autonomous driving systems.
High-definition map annotation from LiDAR data — labeling road topology, lane attributes, traffic infrastructure, and drivable areas at centimeter accuracy for autonomous vehicle navigation systems.
We assign consistent unique IDs to objects across sequential LiDAR frames — enabling AI models to track trajectories, predict motion, and understand behavior over time in dynamic driving and robotic environments.
Tailored solutions for specialized domains
Structured workflow for high-quality spatial datasets
Pre-processing and normalizing raw point cloud and video data.
Setting up project-specific tools and annotator training.
Precise identification and classification of objects in 3D space.
Stringent multi-level QA to ensure 99.5% spatial accuracy.
Final dataset delivery in your preferred format (JSON, CSV, etc.).
Reliable partner for complex 3D data challenges
We ensure centimeter-level accuracy in all 3D bounding box and segmentation tasks.
Access hundreds of trained annotators for large-scale dataset production.
Experience our LiDAR quality firsthand with a free pilot for your project.
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