Eye Tracking Calibration via Scene Understanding in AR Headsets

The calibration system transitions from static initial calibration to dynamic continuous calibration. The system continuously updates eye tracking parameters by detecting real-world objects and comparing their rendered positions with actual sensor detections, allowing the calibration to adapt to HMD deformations and movements during use.

The system implements feedback loops where eye tracking data and object detection data are continuously compared. The difference between rendered object positions and actual sensor detections feeds back into the calibration process, automatically adjusting parameters to maintain accuracy without user intervention.

The system performs calibration selectively rather than continuously at full intensity. It triggers calibration updates based on detected changes in the environment or HMD state, performing partial calibration operations only when necessary to maintain accuracy, thus reducing overall computational load.

The system performs preliminary object detection and rendering to predict where objects should appear before actual eye tracking measurement. This preliminary action allows the system to pre-calculate expected positions and compare them with actual detections, streamlining the calibration process and reducing real-time processing requirements.

The system uses multi-functional sensors that serve multiple purposes. For example, cameras and sensors used for basic AR rendering and environment mapping are also utilized for eye tracking calibration and object detection, eliminating the need for separate dedicated calibration hardware and simplifying manufacturing.

The calibration function is merged with the existing AR rendering pipeline. The same processors that handle graphics rendering and the same sensors that capture the environment are used for calibration tasks, combining multiple functions into unified processing streams to reduce hardware complexity.