Panoramic Image Stitching Radial Distortion Estimation
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Solution Overview
Problem
Conventional image stitching methods fail to accurately estimate lens distortion at the pairwise stage, leading to misalignment issues, especially along image borders, due to the assumption of ideal pinhole models and lack of modeling lens distortion, which affects the quality of panoramic image stitching.
Innovation Solution
A method and apparatus that estimate relative 3D camera rotations, focal lengths, and radial distortions using a minimal number of point-correspondences at the pairwise stage, employing a core estimator and robust estimator to handle noise and errors, allowing for simultaneous estimation of rotation, focal lengths, and radial distortion, thereby addressing lens distortion in panoramic image alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional image stitching methods assume ideal pinhole models and do not model lens distortion, then the device complexity is reduced, but the manufacturing precision of image alignment deteriorates
Solution Approach 1:
The patent changes the mathematical parameters of the transformation model by incorporating radial distortion parameters (k1, k2) into the homography estimation. This allows the model to account for lens distortion effects while maintaining computational efficiency through closed-form solutions.
Solution Approach 2:
The patent segments the transformation model into distinct components: the ideal pinhole projection model and the radial distortion model. This segmentation allows each component to be handled separately and combined, improving both computational efficiency and accuracy.
2Ease of operation
If lens distortion is not modeled at the pairwise stage, then the ease of operation is improved, but the reliability of panoramic stitching deteriorates
Solution Approach 1:
The patent performs preliminary action by estimating and compensating for lens distortion at the pairwise stage before final panoramic composition. This preliminary distortion correction ensures that alignment errors are minimized early in the pipeline, improving final stitching reliability.
3Productivity
If a minimal number of point-correspondences is used for estimation, then the speed of processing is improved, but the measurement precision of distortion parameters deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms through robust estimation techniques that iteratively refine distortion parameters. The algorithm uses inlier identification and iterative optimization to improve measurement precision while maintaining computational efficiency.
Solution Approach 2:
The patent uses a minimal set of point correspondences (excessive action) to establish initial distortion estimates, then refines these estimates through iterative optimization. This approach ensures sufficient data for accurate parameter estimation while maintaining processing speed.
Data Source
AI summary
Method and apparatus for estimating relative three-dimensional (3D) camera rotations, focal lengths, and radial (lens) distortions from point-correspondences in pairwise (two image) image alignment. A core estimator takes a minimal (three) number of point-correspondences and returns a rotation, lens (radial) distortion and two focal lengths. The core estimator solves relative 3D camera rotations, and lens distortions from 3-point-correspondences in two images in the presence of noise in point-correspondences. A robust estimator may be based on or may be “wrapped around” the core estimator to handle noise and errors in point-correspondences. The robust estimator may determine an alignment model for a pair of images from the rotation, distortion, and focal lengths.