3D Point Cloud Annotation Services for Autonomous Driving, Robotics, and Mapping

3D point clouds provide detailed geometric information about environments, enabling perception models to understand depth, structure, and spatial relationships. Autonomous vehicles, mobile robots, drones, and mapping platforms rely on point cloud data to localize themselves, detect objects, and analyze complex surroundings. Accurate point cloud annotation is essential because it allows models to learn the 3D structure of vehicles, pedestrians, obstacles, and environmental features.DataVLab provides 3D point cloud annotation services tailored to ADAS developers, robotics teams, mapping organizations, industrial automation companies, and research groups. Our annotators work within structured guidelines designed to maintain consistency across large scale 3D datasets. These guidelines define segmentation rules, class hierarchies, geometric thresholds, occlusion handling, and multi return LiDAR behavior.We support semantic segmentation, instance segmentation, 3D object labeling, lane and road surface extraction, region classification, vegetation and infrastructure labeling, and multi frame sequence annotation. For multi sensor datasets, we align 3D annotations with corresponding 2D frames or radar signatures to support cross modality perception pipelines.Quality control includes point density inspection, boundary accuracy checks, class consistency validation, and temporal verification across sequences. When required, work can be performed under GDPR aligned workflows with optional EU only annotation.Our 3D point cloud annotation workflows help perception models learn spatial structure with precision and reliability, supporting safe navigation and robust environmental understanding.