Binocular Image Deformation Calibration via Feature Point Detection
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Solution Overview
Problem
Image capturing devices with binocular lenses face challenges in maintaining precise 3D depth information due to lens displacement or rotation, leading to poor 3D photographing effects when factory-default calibration parameters no longer match the actual spatial configuration.
Innovation Solution
An adaptive method for calibrating image deformation by capturing image groups, detecting feature point offsets, and updating calibration parameters using a database of feature point information to dynamically adjust for lens offsets, ensuring accurate image rectification without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory-default calibration parameters are used for binocular lenses, then initial 3D depth information can be obtained, but the calibration parameters become inaccurate when lens displacement or rotation occurs during device operation
Solution Approach 1:
The system performs self-calibration by automatically detecting feature points in captured images, comparing them with stored reference data, and updating calibration parameters without requiring external intervention or specialized calibration equipment. The device serves itself to maintain accuracy despite lens displacement.
Solution Approach 2:
The system captures images, detects feature points, compares detected positions with expected positions based on factory calibration, identifies discrepancies indicating lens displacement, and uses this feedback to automatically update calibration parameters. This closed-loop feedback mechanism ensures continuous accuracy.
2Measurement precision
If manual recalibration is performed after lens displacement, then 3D depth information accuracy can be restored, but user operation complexity increases and calibration time is lost
Solution Approach 1:
The calibration process is completely automated, requiring no user intervention. The system independently detects lens displacement through feature point analysis and performs recalibration automatically, transforming a previously manual task into an autonomous self-service function.
Solution Approach 2:
The system performs calibration actions immediately when displacement is detected, rather than waiting for user initiation. By automatically triggering the recalibration process, the system eliminates delays and operational complexity associated with manual calibration workflows.
3Measurement precision
If feature point detection and comparison is performed continuously, then calibration parameter accuracy is maintained, but device complexity and processing time increase
Solution Approach 1:
Instead of continuously monitoring all parameters, the system performs partial calibration actions only when necessary - specifically when feature point detection indicates lens displacement has occurred. This selective approach maintains accuracy while reducing unnecessary processing complexity.
Solution Approach 2:
The system changes its operational state based on detected conditions: it transitions from normal operation to calibration mode only when feature point analysis reveals displacement beyond acceptable thresholds. This dynamic parameter adjustment optimizes the balance between precision and complexity.
4Reliability
If adaptive calibration parameter updating is implemented, then capturing quality remains consistent despite lens displacement, but data processing requirements and computational load increase
Solution Approach 1:
The system performs full calibration processing only when displacement is detected through feature point comparison. During normal operation with no displacement, the system uses existing calibration parameters without additional computational overhead, thus maintaining quality consistency while minimizing energy consumption.
Solution Approach 2:
The system performs preliminary feature point detection and comparison to determine whether calibration updating is necessary. This preliminary action filters out cases where no displacement occurred, avoiding unnecessary computational energy expenditure while ensuring quality is maintained when needed.
Data Source
AI summary
An image capturing device and a method for calibrating image deformation thereof are provided. The image capturing device has a first image sensor and a second image sensor and the method includes following steps. A plurality of image groups are captured through the first image sensor and the second image sensor. Each of the image groups includes a first image and a second image, and the image groups include a reference image group. Whether an image deformation occurs on a first reference image and a second reference image in the reference image group is detected. If it is detected that the image deformation occurs on the reference image group, a current calibration parameter is updated according to a plurality of feature point comparison values corresponding to the image groups. The current calibration parameter is used for performing an image rectification on each of the first images and the second images.