Multiview Camera Calibration Parameter Correction for Residual Error
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Solution Overview
Problem
Conventional multiview camera calibration methods suffer from residual errors due to assumptions about optical axis alignment and manufacturing tolerances, leading to inaccuracies in three-dimensional reconstruction.
Innovation Solution
A secondary processing step is applied to adjust critical calibration parameters such as yaw, roll, and optical axis independently or collectively to minimize residual errors, using a monotonic function to align with ground truth parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration algorithms are used to estimate intrinsic and extrinsic parameters, then the system can achieve basic three-dimensional depth reconstruction, but residual errors remain due to assumptions about optical axis alignment and manufacturing tolerances
Solution Approach 1:
The patent changes the parameter representation by introducing a monotonic function that maps camera response values to depth values. Instead of relying on traditional intrinsic and extrinsic parameters that assume perfect optical axis alignment, the invention uses a monotonic function parameter that can accommodate real-world manufacturing tolerances and alignment errors while maintaining measurement accuracy
Solution Approach 2:
The patent replaces the mechanical/optical alignment assumptions with a computational model. Instead of requiring precise physical alignment of optical axes with image sensors, the invention uses a monotonic function that computationally corrects for misalignments, substituting mechanical precision requirements with algorithmic compensation
2Ease of manufacture
If traditional stereo calibration is used, then the process can be completed with standard manufacturing tolerances, but the accuracy is insufficient for highly accurate three-dimensional reconstruction requirements
Solution Approach 1:
The patent implements a self-calibration approach where the system uses its own measurement data to automatically refine the monotonic function parameters. The calibration process serves itself by using captured images and extracted features to iteratively improve the depth mapping function without requiring external reference standards or manual adjustment
Solution Approach 2:
The patent performs preliminary calibration to establish an initial monotonic function, then uses this preliminary model to guide subsequent measurements and refinements. The initial calibration setup provides a foundation that enables accurate three-dimensional reconstruction even with standard manufacturing tolerances
Data Source
AI summary
Multiview calibration is essential for accurate three-dimensional computation. However, multiview calibration cannot be accurate enough because of the tolerances required in some of the intrinsic and extrinsic parameters that are associated with the calibration process, along with fundamental imperfections that are associated with the manufacturing and assembly process itself. As a result, residual error in calibration is left over, with no known methods to mitigate such errors. Residual error mitigation of the invention may be performed inline with a given calibration approach, or as a secondary processing step. Residual error mitigation aims at modifying the original parameters that have been estimated during an initial calibration process. These new, modified parameters are then used for triangulation and depth estimation of scene information. This approach also resolves parameter tolerances that are either too cumbersome to measure, or otherwise impossible to measure for practical stereo and multiview camera production and calibration applications.