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.

本研究提出了一种基于 Ackermann 运动模型和简化深度假设的最小算法求解滚动快门相机拍摄影像中的畸变，实现了高效且精确的运动补偿。