Multi-View Stereo Calibration via Sensor Feedback
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Solution Overview
Problem
Multi-view stereo imaging systems with multiple digital cameras face challenges in maintaining calibration over time due to wear and tear, mechanical, thermal, and vibration-related issues, making manual recalibration cumbersome and impractical.
Innovation Solution
A multi-camera error compensating system that detects physical impact events using sensors and automatically recalibrates image calibration parameters, minimizing user interaction by predicting and correcting misalignments through predictive tables and reprocessing of image frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recalibration is performed to maintain image calibration accuracy, then measurement precision is improved, but loss of time and productivity deteriorate due to the cumbersome and impractical nature of manual recalibration
Solution Approach 1:
The system performs automatic recalibration using sensors to detect physical impacts and algorithms to adjust image parameters without requiring manual user intervention. The multi-camera system self-corrects calibration drift by detecting impacts via accelerometers/gyroscopes and automatically recomputing depth maps, eliminating the need for time-consuming manual recalibration while maintaining accuracy.
Solution Approach 2:
The system uses sensors (accelerometers, gyroscopes) to continuously monitor physical conditions and provides feedback to the calibration system. When impacts or vibrations are detected, the system receives feedback about calibration drift and automatically adjusts image parameters accordingly, creating a closed-loop system that maintains accuracy without manual intervention.
2Reliability
If manual recalibration is performed to correct misalignments from wear and tear, mechanical, thermal, and vibration-related issues, then reliability is improved, but ease of operation deteriorates due to the cumbersome process
Solution Approach 1:
The system automatically detects calibration degradation caused by wear, thermal effects, and vibrations using integrated sensors, and performs self-correction through algorithmic adjustment of image parameters. This eliminates the need for users to manually intervene in the recalibration process, making operation simple while maintaining reliability.
Solution Approach 2:
The system replaces manual mechanical recalibration operations with automated sensor-based detection and software-based correction. Instead of requiring physical adjustment of camera mounts or manual alignment procedures, the system uses accelerometers, gyroscopes, and image processing algorithms to detect and correct calibration drift automatically.
3Ease of operation
If automatic sensor-based detection is implemented to detect physical impact events, then ease of operation is improved by minimizing user interaction, but device complexity increases due to additional sensors and processing systems
Solution Approach 1:
The system uses multi-functional components that serve multiple purposes: the same sensors (accelerometers, gyroscopes) used for device orientation and motion tracking are also used to detect physical impacts affecting camera calibration. The image processing algorithms serve both depth mapping and calibration verification functions. This multi-functionality reduces the need for dedicated separate components, mitigating the increase in device complexity.
Solution Approach 2:
The system merges the calibration detection and correction functions with existing device sensors and processing pipelines. Rather than adding completely separate calibration hardware, the patent integrates calibration monitoring into the existing motion sensing and image processing infrastructure, combining multiple functions into unified systems to minimize added complexity.
4Productivity
If predictive analytics and sensor feedback are used for automatic recalibration, then productivity is improved by reducing manual intervention, but device complexity increases due to predictive algorithms and processing requirements
Solution Approach 1:
The system performs preliminary detection of physical impacts using sensors before calibration drift significantly degrades image quality. By detecting impacts early and triggering recalibration proactively, the system maintains accuracy without waiting for manual intervention or significant degradation, improving productivity through preventive rather than reactive calibration.
Solution Approach 2:
The system creates predictive models of calibration drift based on sensor data and historical patterns, using these models to anticipate and correct calibration issues before they manifest in degraded images. This copying of calibration states and predictive modeling enables automated correction without requiring complex real-time analysis of every image parameter.
Data Source
AI summary
A system for determining a loss of calibration in a multi-view stereo imaging system including executing instructions, via a processor, for a multi-view stereo imaging system to process a plural image frame recorded from a plurality of digital cameras of an information handling system and based on plural image calibration parameters and detecting a physical impact event, via a physical sensor, to an information handling system. The system and method execute instructions for a physical impact event detection system to determine, based on physical sensor feedback data, whether a threshold level of a physical impact event has been reached so as to affect calibration of the multi-view stereo imaging system. The detected physical impact event may be a mechanical impact event, a thermal impact event, a vibration mechanical impact event, or another physical impact event.