Physical AI Training Data

3D Sensor Fusion and LiDAR Annotation

Expert 3D point cloud annotation and sensor fusion labeling for autonomous vehicles, robotics, and physical AI, LiDAR, radar, and camera data at scale.

Autonomous systems perceive the world through multiple sensor types simultaneously. The quality of LiDAR annotation and multi-sensor fusion labeling determines whether a perception system understands what it is seeing, or merely detects that something is present. Appen's 3D annotation service delivers the precise, consistent, sensor-fused datasets that safety-critical autonomous vehicle, drone, and robotics applications require.

What Appen Delivers

LiDAR Point Cloud Annotation

3D bounding box placement, instance segmentation, and semantic labeling of LiDAR point cloud data across vehicles, pedestrians, cyclists, infrastructure, and free-space categories. Annotators are trained on the specific sensor characteristics and scene geometry of your data, ensuring label consistency across different point densities and weather conditions.

Radar and Camera Fusion Labeling

Coordinated annotation across LiDAR, radar, and camera feeds for the same scene, with consistent object identities and labels across modalities. Multi-sensor fusion annotation enables perception models that leverage the complementary strengths of each sensor type rather than treating each in isolation.

HD Map Annotation

Labeling of lane boundaries, road markings, traffic signs, and drivable surface areas for high-definition map construction and map-based localisation. HD map annotation requires centimetre-level precision and domain knowledge of traffic rules and road taxonomy.

Temporal Sequence Annotation

Object tracking and trajectory labeling across sequential LiDAR and camera frames, providing the temporal continuity data that perception models require to predict motion and anticipate behaviour over time.

Precision and Safety Standards

Safety-critical applications cannot tolerate annotation variability. Appen's sensor fusion annotation programmes include multiple independent review rounds, geometric consistency checks, and statistical quality sampling to ensure that label accuracy meets the standards that downstream ADAS and autonomous driving validation requires.

In-cabin automotive and world model data programmes can be co-scoped with LiDAR annotation to build complete physical AI datasets in a single engagement.

Related Resources

Blog

Exploring the World of LiDAR: What is it and How Does it Work?

LiDAR has served as a useful tool in many industries for decades, but only recently are we starting to realize its true potential with the introduction of artificial intelligence (AI)-powered solutions. LiDAR, also known as light detection and ranging, is a remote sensing technology.

Read article

Case Study

How Nearmap Scaled AI Data Labeling for Aerial Imagery

Discover how Nearmap partnered with Appen to scale computer vision data annotation for high-volume aerial and 3D imagery.