Binocular Headset Virtual Camera Calibration via Iterative User Feedback
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Solution Overview
Problem
Current virtual reality (VR) and augmented reality (AR) systems face challenges in accurately calibrating virtual camera positions within head-mounted displays (HMDs), leading to misalignments and eye fatigue due to incorrect positioning, which affects the user's perception of virtual objects in relation to their real-world environment.
Innovation Solution
A method involving a processor that presents computer-generated image data on binocular headset displays, iteratively updating the image data based on user feedback to align virtual camera positions with the user's actual eye positions, using graphical elements and quality measures to guide the calibration process, allowing users to intuitively and efficiently adjust the camera positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the virtual camera position is set to a default position in the HMD, then the system is simple to operate, but the virtual objects become misaligned with real-world objects causing eye fatigue
Solution Approach 1:
The system performs preliminary calibration before normal operation by capturing images of a calibration object with known geometric features. This pre-adjustment of the virtual camera position eliminates the need for users to manually adjust settings during operation, resolving the contradiction between ease of operation and alignment accuracy.
Solution Approach 2:
The calibration system uses the user's own eye position and the captured images to automatically determine the correct virtual camera position. The system self-calibrates by processing the calibration images and calculating the optimal camera position without requiring external intervention or complex manual adjustment mechanisms.
2Measurement precision
If manual calibration methods are used to adjust virtual camera position, then alignment precision can be improved, but the calibration process becomes time-consuming and complex
Solution Approach 1:
The system replaces manual mechanical adjustment mechanisms with an automated optical-computational system. A camera captures images of a calibration object, and a processor automatically calculates the optimal virtual camera position based on image analysis, eliminating time-consuming manual adjustments while achieving high precision.
Solution Approach 2:
The system changes the calibration approach from iterative manual parameter adjustment to a single-step computational solution. By capturing a calibration image and processing it algorithmically, the system determines the correct virtual camera position parameters directly, significantly reducing calibration time while maintaining accuracy.
3Ease of operation
If the virtual camera position is not accurately calibrated, then the system operates without adjustment, but virtual objects do not align with real-world objects in AR applications
Solution Approach 1:
The system performs calibration as a preliminary step before normal AR operation. By capturing calibration images and determining the optimal virtual camera position in advance, the system ensures precise alignment of virtual objects with real-world objects during subsequent use without requiring repeated adjustments.
Data Source
AI summary
Computer-generated image data is presented on first and second displays of a binocular headset presuming that a user's left and right eyes are located at first and second positions relative to the first and second displays respectively. At least one updated version of the image data is presented, which is rendered presuming that at least one of the user's left and right eyes is located at a position different from the first and second positions respectively in at least one spatial dimension. In response thereto, a user-generated feedback signal is received expressing either: a quality measure of the updated version of the computer-generated image data relative to computer-generated image data presented previously; or a confirmation command. The steps of presenting the updated version of the computer-generated image data and receiving the user-generated feedback signal are repeated until the confirmation command is received. The first and second positions are defined based on the user-generated feedback signal.