Multi-View Camera Registration Using 3D Back-Projection Feedback
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Solution Overview
Problem
Existing methods for creating accurate 3D models using camera registration are limited by the need for precise camera parameters and lack of efficient error metric calculation, leading to suboptimal registration and model accuracy.
Innovation Solution
A system for multi-view camera registration that automatically adjusts camera parameters and 3D model geometry by comparing back-projected images using color space comparisons to minimize error metrics, incorporating techniques like Structure from Motion and Bundle Adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera registration methods are used, then the process is simpler, but the accuracy of 3D model creation deteriorates
Solution Approach 1:
The patent segments the camera registration process into multiple iterative stages: initial parameter estimation, back-projection of images to 3D model, error metric calculation through color space comparison, and parameter adjustment. Each stage processes specific aspects of the registration independently, improving overall accuracy without requiring complete process redesign.
Solution Approach 2:
The patent implements a feedback mechanism where error metrics are calculated by comparing back-projected images with actual images from multiple camera views. These error metrics feed back into the parameter adjustment process, continuously refining camera parameters and 3D model geometry to minimize registration errors and improve model accuracy.
2Measurement precision
If precise camera parameters are required, then model accuracy improves, but the difficulty of detecting and measuring parameters increases
Solution Approach 1:
The patent replaces traditional mechanical measurement methods with computational approaches. Instead of physically measuring camera parameters with precision instruments, the system uses color space comparisons of images and automated error metric calculations to determine and refine parameter values, significantly reducing measurement difficulty while maintaining precision.
Solution Approach 2:
The system performs self-calibration by automatically adjusting camera parameters based on image comparisons and error metrics. The registration process serves itself by using the captured images and computational algorithms to refine parameters without requiring external calibration targets or manual intervention, simplifying the measurement process.
3Reliability
If multiple camera views are integrated, then model completeness improves, but the complexity of aligning and registering views increases
Solution Approach 1:
The patent merges multiple camera views by back-projecting images from different angles onto a unified 3D model framework. The color space comparison method combines information from all views simultaneously, integrating their contributions to create a complete and consistent 3D representation while managing alignment complexity through standardized processing.
Solution Approach 2:
The patent implements a universal registration framework that handles multiple camera views with different parameters (focal length, pan angle, tilt angle, zoom level, XYZ position) using the same color space comparison and error metric minimization approach. This multi-functional system can process any number of camera views with varying configurations through a single unified method.
Data Source
AI summary
A system for registering one or more cameras and/or creating an accurate three-dimensional (3D) model of a world space environment including back projecting at least one image from at least one of a plurality of camera views to the 3D model based on a set of existing camera parameters. The back projected image is added as a texture for the 3D model. This texture is automatically compared to one or more images from other camera views using a color space comparison of images to determine a set of differences or errors. The camera parameters and the 3D model are automatically adjusted to minimized the differences or errors. Over time, the parameters and the 3D model converge on a state that can be used to track moving objects, insert virtual graphics and/or perform other functions.