Cuboid labeling and point-cloud segmentation for autonomous vehicles, drones, and robotics — annotated with sensor-fusion accuracy against paired camera frames.
Precise 3D bounding boxes around vehicles, pedestrians, and obstacles in point-cloud scenes.
Point-level classification of road, terrain, vegetation, and structures for scene understanding.
LiDAR frames cross-referenced with paired camera imagery for higher-confidence object classification.
Consistent object IDs tracked across sequential point-cloud frames for motion prediction models.
LiDAR annotation is core to how autonomous vehicles, delivery robots, and drones perceive the world in three dimensions. See our dedicated autonomous driving annotation page for the full perception stack.
A short clip from real annotation output — 3D cuboids tracked across a raw LiDAR point cloud, viewed from front, side, top, and rear.
We work with common formats including PCD, LAS/LAZ, and raw binary point-cloud exports, and can adapt to custom sensor formats.
Yes — sensor-fusion projects are one of our core capabilities, cross-referencing camera frames with point clouds for higher-confidence labels.
Annotators are calibrated against your specific sensor's noise characteristics during the pilot phase before full production begins.
Share your requirements to discuss a suitable pilot and proposal scope.