Tight-fitting 3D cuboids expertly applied to point cloud clusters to accurately represent the physical volume of real-world objects.

Rich attribute tagging for each cuboid, including object class, size dimensions, and varying states of occlusion or truncation.

Consistent instance ID tracking across sequential frames to train robust AI models for object permanence and motion forecasting.

Rigorous quality control workflows to eliminate “floating” cuboids and ensure exact alignment with the ground plane and point clusters.