Multi-Camera Rig Calibration via 3D Model Error Minimization
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Solution Overview
Problem
Multi-camera systems in Virtual Reality (VR) systems face calibration challenges due to design and manufacturing tolerances, leading to image distortion and double vision, which degrade user experience.
Innovation Solution
A camera calibration system that models objects seen by multiple cameras, compares them to known surroundings, and adjusts calibration parameters such as rotation and translation to minimize errors, using a gradient descent function to optimize camera positions and orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If cameras are manufactured with standard tolerances, then manufacturing cost and ease of assembly are improved, but image accuracy and visual quality deteriorate due to positioning and orientation errors
Solution Approach 1:
The patent applies preliminary action by performing calibration before the multi-camera system is deployed for its intended purpose. The calibration process determines actual position and orientation parameters for each camera relative to a coordinate system, allowing the system to compensate for manufacturing tolerances. This preliminary calibration step ensures that even though cameras are manufactured with standard tolerances, the final image quality and accuracy requirements are met through software-based correction.
2Measurement precision
If calibration processes are made more accurate, then image quality and visual artifacts are improved, but calibration time and computational resources worsen
Solution Approach 1:
The patent applies parameter changes by using an iterative optimization algorithm that adjusts calibration parameters (position and orientation) to minimize an error function. The process starts with initial estimated parameters and refines them through multiple iterations, changing parameters incrementally to converge on optimal values. This approach balances measurement precision with calibration time by stopping iterations when the error function reaches a minimum or threshold is met, rather than requiring exhaustive precision.
Solution Approach 2:
The patent implements feedback through an error function that evaluates the quality of calibration parameters by comparing captured images with expected geometric relationships. The optimization algorithm uses this feedback to adjust parameters in subsequent iterations, reducing visual artifacts like double vision and distortion. The feedback mechanism allows the system to automatically refine calibration without requiring manual intervention or excessively long processing times.
3Reliability
If multiple cameras are calibrated jointly, then overall system accuracy is improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent applies segmentation by dividing the calibration process into manageable components. The error function is constructed as a sum of individual error terms, each corresponding to a specific camera or image pair. The optimization process can potentially be applied to subsets of cameras or iteratively refined in stages, making the complex multi-camera calibration problem more tractable while still achieving joint optimization of all camera parameters for improved system accuracy.
Data Source
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AI summary
A camera calibration system jointly calibrates multiple cameras in a camera rig system. The camera calibration system obtains configuration information about the multiple cameras in the camera rig system, such as position and orientation for each camera relative to other cameras. The camera calibration system estimates calibration parameters (e.g., rotation and translation) for the multiple cameras based on the obtained configuration information. The camera calibration system receives 2D images of a test object captured by the multiple cameras and obtains known information about the test object such as location, size, texture and detailed information of visually distinguishable points of the test object. The camera calibration system then generates a 3D model of the test object based on the received 2D images and the estimated calibration parameters. The generated 3D model is evaluated in comparison with the actual test object to determine a calibration error. The calibration parameters for the cameras are updated to reduce the calibration error for the multiple cameras.