Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations
This work addresses the problem of automated image rectification for man-made environments, offering incremental improvements in robustness and efficiency for computer vision applications.
The paper tackles the problem of jointly solving for radial lens undistortion and affine-rectification from images of coplanar translated and reflected scene texture, introducing minimal solvers that require as few as one feature correspondence and show superior robustness to noise in experiments.
This paper introduces minimal solvers that jointly solve for radial lens undistortion and affine-rectification using local features extracted from the image of coplanar translated and reflected scene texture, which is common in man-made environments. The proposed solvers accommodate different types of local features and sampling strategies, and three of the proposed variants require just one feature correspondence. State-of-the-art techniques from algebraic geometry are used to simplify the formulation of the solvers. The generated solvers are stable, small and fast. Synthetic and real-image experiments show that the proposed solvers have superior robustness to noise compared to the state of the art. The solvers are integrated with an automated system for rectifying imaged scene planes from coplanar repeated texture. Accurate rectifications on challenging imagery taken with narrow to wide field-of-view lenses demonstrate the applicability of the proposed solvers.