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3D Point Cloud Annotation

Precise 3D labeling for LiDAR, RADAR, and multi-sensor data. Cuboid annotation, semantic segmentation, and sensor fusion for autonomous vehicles, robotics, and spatial intelligence.

3D point cloud and LiDAR annotation

3D Perception Starts With Precise Labeling

Autonomous vehicles, delivery robots, and industrial automation systems rely on 3D perception models trained on meticulously annotated point cloud data. Our specialized annotators work with LiDAR scans, RADAR returns, and fused multi-sensor data to create the 3D training datasets these systems need. We annotate objects with oriented 3D cuboids, classify individual points into semantic categories, and track objects across sequential scans — all while maintaining the spatial precision that safety-critical applications demand.

  • Oriented 3D cuboid annotation with heading and velocity
  • Point-level semantic segmentation (road, sidewalk, vegetation)
  • LiDAR-camera sensor fusion with cross-modal alignment
  • Sequential tracking with consistent object IDs
  • Lane marking and drivable area annotation
Capabilities

3D Annotation Methods

Purpose-built workflows for the complexity of three-dimensional spatial data.

3D Cuboid Annotation

Tight-fitting oriented bounding boxes around vehicles, pedestrians, cyclists, and obstacles in point cloud space. Each cuboid includes position (x, y, z), dimensions (l, w, h), heading angle, and attribute tags for occlusion, truncation, and object state.

Semantic Segmentation

Every point classified into categories like road surface, sidewalk, vegetation, building, vehicle, and pedestrian. Enables scene understanding models to build complete environmental representations for safe navigation.

Sensor Fusion

Synchronized annotation across LiDAR point clouds, camera images, and RADAR data. We project 3D cuboids onto 2D camera views and verify cross-modal consistency, ensuring your fusion models train on perfectly aligned multi-sensor labels.

Sequential Tracking

Object identity maintained across hundreds of consecutive LiDAR scans. Track IDs persist through occlusions and partial visibility, with velocity and trajectory metadata annotated for motion prediction model training.

Lane & Road Marking

3D polyline annotation for lane boundaries, crosswalks, stop lines, and road edges. Includes lane type classification (solid, dashed, double) and connectivity information for HD map construction.

Panoptic Segmentation

Combining semantic segmentation (stuff classes) with instance segmentation (thing classes) for complete scene decomposition. Each point receives both a category label and an instance ID where applicable.

FAQ

Frequently Asked Questions

We support point cloud data from all major LiDAR manufacturers including Velodyne, Ouster, Hesai, Luminar, and Waymo. We work with standard formats like PCD, PLY, LAS/LAZ, and binary point cloud files. Our platform handles both spinning and solid-state LiDAR data natively.
Our 3D cuboid annotations achieve positional accuracy within 10cm and heading angle accuracy within 5 degrees for well-visible objects. We use multi-view verification (top-down, side, front, and camera projections) to ensure tight-fitting cuboids, and every annotation passes through our quality pipeline with IoU-based automated validation.
Yes. We routinely annotate sequences of hundreds of consecutive scans with consistent tracking. Our workflow uses AI-assisted propagation between frames, with human annotators verifying and correcting object positions, handling new objects entering the scene, and maintaining track continuity through occlusion events.
Absolutely. Beyond autonomous driving, we annotate 3D point clouds for warehouse robotics, indoor mapping, construction site monitoring, and agricultural applications. Our teams adapt taxonomies and quality criteria to each domain's specific requirements.
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Label Your 3D Data With Spatial Precision

Send us sample LiDAR scans and we'll return annotated point clouds with cuboids, segmentation, or tracking — free pilot, no commitment.

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