# Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations

arXiv: https://arxiv.org/abs/1911.01507 · HTML: https://mlsanity.com/paper/1911.01507/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [GeoCalib: Learning Single-image Calibration with Geometric Optimization](https://mlsanity.com/paper/2409.06704/) (2024)
- [BabelCalib: A Universal Approach to Calibrating Central Cameras](https://mlsanity.com/paper/2109.09704/) (2021)
- [CalibAnyView: Beyond Single-View Camera Calibration in the Wild](https://mlsanity.com/paper/2605.14615/) (2026)
- [On Relative Pose Recovery for Multi-Camera Systems](https://mlsanity.com/paper/2102.11996/) (2021)
- [Recurrence-based Vanishing Point Detection](https://mlsanity.com/paper/2412.20666/) (2024)
- [3D Reconstruction from public webcams](https://mlsanity.com/paper/2108.09476/) (2021)
- [Matching with AffNet based rectifications](https://mlsanity.com/paper/2207.14660/) (2022)

## Abstract (arXiv)

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.