Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion

TL;DR

This paper introduces a minimal solver for monocular rolling shutter correction in vehicles, leveraging Ackermann motion assumptions to achieve real-time, accurate compensation with a simple four-line algorithm.

Contribution

It presents a novel minimal solver that uses vehicle motion models to efficiently correct rolling shutter distortions in monocular images.

Findings

The solver operates in real-time with high accuracy.

It outperforms existing methods in both qualitative and quantitative evaluations.

The approach is effective on real and simulated datasets.

Abstract

Modern automotive vehicles are often equipped with a budget commercial rolling shutter camera. These devices often produce distorted images due to the inter-row delay of the camera while capturing the image. Recent methods for monocular rolling shutter motion compensation utilize blur kernel and the straightness property of line segments. However, these methods are limited to handling rotational motion and also are not fast enough to operate in real time. In this paper, we propose a minimal solver for the rolling shutter motion compensation which assumes known vertical direction of the camera. Thanks to the Ackermann motion model of vehicles which consists of only two motion parameters, and two parameters for the simplified depth assumption that lead to a 4-line algorithm. The proposed minimal solver estimates the rolling shutter camera motion efficiently and accurately. The extensive experiments on real and simulated datasets demonstrate the benefits of our approach in terms of qualitative and quantitative results.