Multi-View Camera Registration Through 3D Model Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera registration methods struggle to accurately create a 3D model of world space using multiple camera views, particularly when dealing with moving cameras or environments where precise camera parameters are needed for tasks like virtual graphics insertion and object tracking.
Innovation Solution
A system that performs multi-view camera registration by back-projecting images onto a 3D model, comparing them using color space analysis to adjust camera parameters and model geometry to minimize errors, employing techniques like Structure from Motion and Bundle Adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple camera views are registered simultaneously to create a 3D model, then the accuracy of 3D model creation and camera parameter estimation is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the camera registration process into iterative refinement steps, where initial camera parameters are estimated separately for each view, then progressively refined by comparing back-projected images across multiple views. This divides the complex simultaneous registration problem into manageable sequential steps that converge to an optimal solution.
Solution Approach 2:
The patent implements feedback by computing error metrics between back-projected images from different camera views and using these errors to iteratively adjust camera parameters. The error computation provides feedback that guides the optimization process, allowing the system to converge on accurate parameter estimates while managing computational load through controlled iteration.
2Manufacturing precision
If iterative refinement is performed to minimize image differences, then the accuracy of virtual graphics insertion and object tracking is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by performing a limited number of iterative refinement steps rather than exhaustive optimization. The system computes error metrics and adjusts parameters for a predetermined number of iterations or until convergence criteria are met, achieving sufficient accuracy for virtual graphics insertion and object tracking without unnecessary computational overhead.
Solution Approach 2:
The patent performs preliminary action by establishing initial camera parameters and 3D model estimates before iterative refinement. Initial camera parameters are estimated from individual views, and a preliminary 3D model is constructed, providing a starting point that reduces the number of refinement iterations needed to achieve final precision.
3Reliability
If color space comparison is used to compute error metrics, then the robustness of image alignment is improved, but the computational requirements increase
Solution Approach 1:
The patent changes parameters by working in color space rather than raw pixel intensity space when computing error metrics. This parameter transformation provides robustness to lighting variations and color inconsistencies across different camera views, improving alignment reliability. The system computes color differences in a transformed space that is more tolerant of environmental variations.
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.