Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation?

TL;DR

This paper demonstrates that a simple approach combining pinhole solvers with sampled radial distortion parameters can match or outperform complex minimal radial distortion solvers in relative pose estimation, offering faster and more accurate results.

Contribution

The study shows that complex minimal radial distortion solvers are unnecessary, proposing a simple, efficient alternative that achieves comparable or better accuracy in practice.

Findings

Sampled radial distortion approach performs similarly or better than complex solvers.

The simple method is faster and more accurate than non-minimal solvers.

Complex radial distortion solvers are not needed for effective relative pose estimation.

Abstract

Estimating the relative pose between two cameras is a fundamental step in many applications such as Structure-from-Motion. The common approach to relative pose estimation is to apply a minimal solver inside a RANSAC loop. Highly efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit radial distortion. Not modeling radial distortion leads to (significantly) worse results. However, minimal radial distortion solvers are significantly more complex than pinhole solvers, both in terms of run-time and implementation efforts. This paper compares radial distortion solvers with a simple-to-implement approach that combines an efficient pinhole solver with sampled radial distortion parameters. Extensive experiments on multiple datasets and RANSAC variants show that this simple approach performs similarly or better than the most accurate minimal distortion solvers at faster run-times while being significantly more accurate than faster non-minimal solvers. We clearly show that complex radial distortion solvers are not necessary in practice. Code and benchmark are available at https://github.com/kocurvik/rd.