Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

TL;DR

This paper introduces minimal solvers for auto-calibrating lens-distorted cameras from single images by leveraging scene symmetries and lines, improving accuracy through combined feature use and RANSAC integration.

Contribution

The paper presents novel minimal solvers that jointly estimate lens distortion and camera parameters using scene symmetries and lines, enhancing calibration accuracy.

Findings

Solvers outperform existing methods on urban image datasets.

Combining multiple feature types improves calibration accuracy.

Proposed methods are effective and complementary in RANSAC frameworks.

Abstract

This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at https://github.com/ylochman/single-view-autocalib.