Dynamic Parallax Correction for AR Sensor Fusion
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Solution Overview
Problem
Conventional augmented reality and computer vision systems face challenges with image distortion caused by spherical lenses and double vision due to misregistration of multiple sensor viewpoints at varying focal depths, which complicates navigation and target identification.
Innovation Solution
The system employs a depth tracker and precalculated look-up-tables (LUTs) to correct parallax and distortion errors by interpolating between LUTs precomputed for specific focal depths, generating an interpolated LUT function to dynamically correct images in real-time, ensuring accurate image registration across varying focal depths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple image sensors are used to capture images from different viewpoints, then depth perception and navigation capability are improved, but parallax error and image misregistration occur causing double vision
Solution Approach 1:
The patent introduces an intermediary computational process that uses depth information as a mediator to adjust and align images from multiple sensors. By calculating depth values for different regions and using these as adjustment parameters, the system dynamically corrects parallax errors and achieves accurate image registration across varying focal depths.
Solution Approach 2:
The system dynamically changes image parameters (position, scale, orientation) based on depth values. By adjusting these parameters according to the calculated depth of different regions, the system compensates for parallax effects and maintains accurate registration across multiple sensor viewpoints at varying focal depths.
2Adaptability or versatility
If spherical lenses are used in image sensors, then image capture capability is improved, but distortion error is introduced
Solution Approach 1:
The patent introduces depth information as an intermediary to mediate between the distorted lens images and the final corrected output. By using depth values as adjustment parameters, the system dynamically compensates for lens-induced distortion while preserving the benefits of spherical lens image capture.
Solution Approach 2:
The system creates a composite correction approach by combining multiple correction techniques: parallax correction based on sensor geometry, distortion correction based on lens characteristics, and depth-based dynamic adjustment. This composite approach addresses both lens distortion and parallax effects simultaneously.
3Speed
If conventional distortion correction methods are used, then processing speed is improved, but accuracy varies significantly at different focal depths
Solution Approach 1:
The patent transforms static correction methods into dynamic ones by continuously adjusting correction parameters based on real-time depth information. The system dynamically modifies image adjustment parameters according to the focal depth of different regions, maintaining high accuracy across varying depths while preserving processing efficiency through optimized computational approaches.
Solution Approach 2:
The system changes correction parameters dynamically based on depth values. By adjusting position, scale, and orientation parameters according to the calculated depth of each region, the system maintains accurate correction across varying focal depths rather than using fixed parameters that work well only at specific depths.
4Manufacturing precision
If real-time dynamic correction is implemented, then image accuracy at varying depths is improved, but computational complexity and processing load increase
Solution Approach 1:
The patent divides the image processing into segmented regions with different depth characteristics. By calculating depth values for specific regions and applying targeted corrections only where needed, the system reduces overall computational complexity while maintaining accuracy in critical areas. This segmented approach avoids the need for exhaustive processing of entire images at full resolution.
Solution Approach 2:
The system performs preliminary depth calculation and region identification before applying detailed correction algorithms. By pre-calculating depth values and identifying regions requiring correction, the system reduces the computational burden of subsequent processing steps while ensuring accurate correction is applied where most needed.
Data Source
AI summary
An augmented reality (AR) vision system is disclosed. A display is configured to present a surrounding environment to eyes of a user of the AR vision system. A depth tracker is configured to produce a measurement of a focal depth of a focus point in the surrounding environment. Two or more image sensors receive illumination from the focus point and generate a respective image. A controller receives the measurement of the focal depth, generates an interpolated look-up-table (LUT) function by interpolating between two or more precalculated LUTs, applies the interpolated LUT function to the images to correct a parallax error and a distortion error at the measured focal depth, generates a single image of the surrounding environment, and displays the single image to the user.