AR Display Color Calibration Using Common Objects
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Solution Overview
Problem
Augmented reality systems require frequent recalibration to maintain accurate color reproduction, as one-time factory calibration cannot account for changes over time, and traditional methods disrupt the user experience by requiring them to view color charts.
Innovation Solution
A calibrated forward-facing camera or spectrometer continuously captures real-world scenes, recognizing known objects from a database to recalibrate the display automatically or with user intervention, using methods like displaying multiple color options for selection or comparing real-world and synthetic imagery colors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color calibration methods are used, then color accuracy can be maintained, but user experience is disrupted by requiring users to view color charts
Solution Approach 1:
The system performs automatic color calibration using common objects already present in the user's field of view. The camera captures images, the processor identifies objects from a database, and the system automatically adjusts color parameters without requiring user intervention or disruption to the normal viewing experience.
Solution Approach 2:
Common objects in the real world serve as intermediaries between the display system and the calibration process. Instead of using traditional color charts, the system uses familiar objects (products, logos, natural artifacts) that the user is already viewing, thereby maintaining natural interaction while achieving calibration.
2Ease of manufacture
If one-time factory calibration is performed, then initial color accuracy is achieved, but calibration cannot account for changes in the display over time
Solution Approach 1:
The system performs continuous or periodic color calibration by continuously monitoring common objects in the field of view. The camera captures images ongoing, and the processor periodically compares captured colors with database values to detect drift and trigger recalibration when needed, ensuring long-term color accuracy.
Solution Approach 2:
The system establishes a feedback loop where the camera continuously captures images of common objects, the processor compares captured colors with known database values, and the display parameters are automatically adjusted when deviations are detected. This closed-loop system accounts for display changes over time.
3Measurement precision
If frequent calibration is performed, then color accuracy is maintained, but traditional methods require users to view color charts which diminishes user experience
Solution Approach 1:
The system performs calibration automatically in the background without requiring user time or attention. The camera and processor work autonomously to detect color drift and perform recalibration, eliminating the need for users to stop their activities to view color charts or interact with calibration interfaces.
Solution Approach 2:
Common objects serve as natural calibration targets that are already part of the user's viewing experience. By using these objects instead of dedicated color charts, the system performs frequent calibration without requiring users to switch their attention to specialized calibration materials, thus minimizing time loss.
Data Source
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AI summary
Described herein are systems and methods for maintaining color calibration using common objects. In an exemplary embodiment, an AR system includes a forward-facing camera, an AR display, a processor, and a user interface. The processor is configured to receive image data from the forward-facing camera and identify any known objects depicted in the image data. The processor then determines RGB information at least one test rendering of the identified known object and displays it via the AR display. Input from a user interface, indicating which of the at least one test renderings was a closest match to the real-world object, and a level of satisfaction with the match are received by the processor and used to update an AR display color calibration model. More test renderings may be iteratively provided to improve the accuracy of the calibration.