Virtual Camera Calibration via Image Quality Optimization
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Solution Overview
Problem
Existing virtual camera systems for motor vehicles require accurate knowledge of real camera positions and directions to produce high-quality images from a bird's eye perspective, but existing methods rely on a model of the real world and are prone to errors due to vibrations, aging, and mechanical stresses, limiting their ability to maintain image quality independently.
Innovation Solution
A method for automatically calibrating a virtual camera system that operates independently of a real-world model, using a combination of real recording cameras with wide-angle or fisheye lenses, an image data processing device, and a display device, which applies nonlinear transformations to subimage data to correct for lens distortions and maintain image quality by recurrent calibration based on quality criteria and vehicle state variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model of the real world (vehicle skin shape) is used for calibration, then calibration can be performed using known geometric information, but the system becomes vulnerable to errors from vibrations, aging, and mechanical stresses that distort the model
Solution Approach 1:
The patent uses virtual copies (synthetic images) of the captured scene generated by the calibration algorithm, rather than relying on physical model copies. These virtual images are created by back-projecting captured image data through the estimated camera parameters, allowing calibration without depending on physical vehicle skin models that are susceptible to environmental degradation
Solution Approach 2:
The patent replaces the mechanical/physical calibration approach (using actual vehicle skin geometry and camera positions) with a computational/optical approach. The calibration is performed by optimizing image quality metrics through image processing and algorithmic transformation, substituting physical measurement with digital image analysis
2Adaptability or versatility
If frequent recalibration is performed to maintain image quality, then adaptability to changing conditions improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements periodic recalibration at predetermined intervals rather than continuous calibration. The system automatically triggers calibration based on time-based or condition-based thresholds, balancing the need for adaptability with computational efficiency by recalibrating only when necessary
Solution Approach 2:
The system uses feedback from image quality assessment to determine when recalibration is needed. By monitoring metrics such as overlap region quality and transformation consistency, the system can intelligently trigger calibration only when degradation is detected, optimizing the balance between adaptability and processing time
Data Source
AI summary
A method for producing a time sequence of images of motor vehicle surroundings from a virtual elevated central perspective on a display wherein several cameras are used to record time sequences of subimage data records from a plurality of real perspectives differently offset and tilted with respect to the virtual perspective. Then, parameterized transformations are applied for obtaining sequences of transformed subimage data records and overall image data for an overall image from the virtual perspective is assembled. Finally, the sequence of the overall image data is displayed as an overall image. To compensate for positional and/or orientation deviations in the recording cameras a selection unit is proposed to recurrently apply a selection criterion for selecting a subimage data record from the sequence and to take this as a basis for redetermining the parameters of the transformations by an optimization method under a quality criterion for the overall image.