Array Camera Dynamic Calibration for Geometry Drift Correction
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Solution Overview
Problem
Existing camera arrays face challenges in maintaining accurate geometric calibration due to changes in the relative positions and orientations of cameras caused by thermal and environmental factors, leading to degraded depth estimates and image quality in applications like augmented reality and machine vision.
Innovation Solution
A dynamic calibration process that utilizes feature matching and residual vector analysis to update geometric calibration data, accounting for changes in camera geometry by using interpolation and extrapolation to generate a residual vector calibration field, and applying scene-dependent corrections to ensure accurate image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera arrays are used to capture light field images, then depth estimation capability is improved, but geometric calibration accuracy deteriorates due to thermal and environmental factors causing changes in camera positions and orientations
Solution Approach 1:
The patent implements dynamic calibration that continuously adapts to changing camera geometries caused by thermal and environmental factors. Instead of relying on static pre-calibration data, the system performs real-time calibration updates by detecting features in captured images, computing residual vectors between observed and expected feature locations, and updating calibration parameters to compensate for drift, thereby maintaining geometric calibration accuracy under dynamic conditions
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring feature correspondence between images from different cameras, computing residual vectors that indicate calibration errors, and using these residuals to update calibration parameters. This closed-loop feedback ensures that depth estimation accuracy is maintained despite thermal and environmental changes affecting camera positions and orientations
2Device complexity
If static geometric calibration data is used, then device complexity is reduced, but depth estimation reliability deteriorates under varying thermal and environmental conditions
Solution Approach 1:
The calibration system performs self-calibration by automatically detecting features in captured images, computing residual vectors, and updating its own calibration parameters without requiring external intervention or complex manual recalibration procedures. This self-service approach maintains reliability under varying conditions while keeping the system relatively simple
3Reliability
If dynamic calibration with feature matching is implemented, then geometric calibration accuracy is maintained under environmental changes, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs calibration updates only when necessary, using feature matching and residual vector computation selectively rather than continuously processing all image data. By applying calibration corrections only to the extent needed to compensate for detected drift, the system maintains geometric calibration accuracy while limiting the increase in processing complexity
Data Source
AI summary
Systems and methods for dynamically calibrating an array camera to accommodate variations in geometry that can occur throughout its operational life are disclosed. The dynamic calibration processes can include acquiring a set of images of a scene and identifying corresponding features within the images. Geometric calibration data can be used to rectify the images and determine residual vectors for the geometric calibration data at locations where corresponding features are observed. The residual vectors can then be used to determine updated geometric calibration data for the camera array. In several embodiments, the residual vectors are used to generate a residual vector calibration data field that updates the geometric calibration data. In many embodiments, the residual vectors are used to select a set of geometric calibration from amongst a number of different sets of geometric calibration data that is the best fit for the current geometry of the camera array.