Learning Structure-And-Motion-Aware Rolling Shutter Correction

Publication Date: 6/16/2019

Event: IEEE Computer Vision and Pattern Recognition (CVPR 2019)

Reference: pp. 4551-4560, 2019

Authors: Bingbing Zhuang, National University of Singapore, NEC Laboratories America, Inc.; Quoc-Huy Tran, NEC Laboratories America, Inc.; Pan Ji, NEC Laboratories America, Inc.; Loong Fah Cheong, National University of Singapore; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: An exact method of correcting the rolling shutter (RS) effect requires recovering the underlying geometry, i.e. the scene structures and the camera motions between scanlines or between views. However, the multiple-view geometry for RS cameras is much more complicated than its global shutter (GS) counterpart, with various degeneracies. In this paper, we first make a theoretical contribution by showing that RS two-view geometry is degenerate in the case of pure translational camera motion. In view of the complex RS geometry, we then propose a Convolutional Neural Network (CNN)-based method which learns the underlying geometry (camera motion and scene structure) from just a single RS image and perform RS image correction. We call our method structure-and-motion-aware RS correction because it reasons about the concealed motions between the scanlines as well as the scene structure. Our method learns from a large-scale dataset synthesized in a geometrically meaningful way where the RS effect is generated in a manner consistent with the camera motion and scene structure. In extensive experiments, our method achieves superior performance compared to other state-of-the-art methods for single image RS correction and subsequent Structure from Motion (SfM) applications.

Publication Link: https://openaccess.thecvf.com/content_CVPR_2019/html/Zhuang_Learning_Structure-And-Motion-Aware_Rolling_Shutter_Correction_CVPR_2019_paper.html