Multi-User 3D Display Gaze Tracking for Personalized Rendering
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Solution Overview
Problem
Existing 3D displays often project images without considering the position and gaze direction of multiple viewers, leading to distorted or broken images due to inadequate viewing angles or distances, wasting processing resources and resulting in a poor user experience.
Innovation Solution
Implementing deep learning systems for face detection, gaze tracking, and camera image evaluation to determine the point of regard, eye state, and position of each viewer relative to the display, allowing for personalized 3D image projections tailored to each viewer's position and gaze direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D displays project images without considering viewer position and gaze direction, then the display system is simple and processing resources are saved, but the images become distorted or broken due to inadequate viewing angles or distances
Solution Approach 1:
The system performs preliminary gaze tracking and viewer position detection before image projection to determine optimal projection parameters. By pre-calculating the point of regard and viewing angle for each viewer, the system can prepare personalized 3D image projections that are guaranteed to be rendered correctly, avoiding distorted or broken images during actual viewing.
Solution Approach 2:
The system continuously monitors viewer position and gaze direction through cameras and feedback mechanisms, adjusting the 3D image projection parameters in real-time. This closed-loop feedback ensures that the projected images remain accurate and undistorted by adapting to changes in viewer position, head orientation, and eye gaze direction.
2Productivity
If the system processes all camera image feeds for gaze tracking, then complete viewer information is obtained, but processing resources are wasted on useless or low-quality image feeds
Solution Approach 1:
The system extracts and processes only the essential information from camera image feeds that is necessary for determining viewer position and gaze direction. By filtering out redundant or low-quality image data and focusing computation only on extracting critical parameters such as eye coordinates, head pose, and point of regard, the system maintains complete viewer information while significantly reducing processing overhead.
Solution Approach 2:
The system applies different processing quality levels to different camera feeds based on their usefulness. High-quality image feeds from cameras with optimal viewing angles receive full processing for precise gaze tracking, while lower-quality feeds from suboptimal positions receive reduced processing or are discarded entirely, conserving computational resources while maintaining accurate viewer information where available.
Data Source
AI summary
Methods, systems, and storage media for projecting multi-viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of image projections for display by the single 3D display.