  This paper addresses the problem of rolling shutter correction in complex
nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer
from two main drawbacks. Firstly, they face challenges in estimating the
accurate correction field due to the uniform velocity assumption, leading to
significant image correction errors under complex motion. Secondly, the drastic
occlusion in dynamic scenes prevents current solutions from achieving better
image quality because of the inherent difficulties in aligning and aggregating
multiple frames. To tackle these challenges, we model the curvilinear
trajectory of pixels analytically and propose a geometry-based Quadratic
Rolling Shutter (QRS) motion solver, which precisely estimates the high-order
correction field of individual pixels. Besides, to reconstruct high-quality
occlusion frames in dynamic scenes, we present a 3D video architecture that
effectively Aligns and Aggregates multi-frame context, namely, RSA2-Net. We
evaluate our method across a broad range of cameras and video sequences,
demonstrating its significant superiority. Specifically, our method surpasses
the state-of-the-art by +4.98, +0.77, and +4.33 of PSNR on Carla-RS, Fastec-RS,
and BS-RSC datasets, respectively. Code is available at
https://github.com/DelinQu/qrsc.
