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野外环境下的快速卷帘快门校正

Fast Rolling Shutter Correction in the Wild.

作者信息

Qu Delin, Liao Bangyan, Zhang Huiqing, Ait-Aider Omar, Lao Yizhen

出版信息

IEEE Trans Pattern Anal Mach Intell. 2023 Oct;45(10):11778-11795. doi: 10.1109/TPAMI.2023.3284847. Epub 2023 Sep 5.

DOI:10.1109/TPAMI.2023.3284847
PMID:37307189
Abstract

This paper addresses the problem of rolling shutter correction (RSC) in uncalibrated videos. Existing works remove rolling shutter (RS) distortion by explicitly computing the camera motion and depth as intermediate products, followed by motion compensation. In contrast, we first show that each distorted pixel can be implicitly rectified back to the corresponding global shutter (GS) projection by rescaling its optical flow. Such a point-wise RSC is feasible with both perspective and non-perspective cases without the pre-knowledge of the camera used. Besides, it allows a pixel-wise varying direct RS correction (DRSC) framework that handles locally varying distortion caused by various sources, such as camera motion, moving objects, and even highly varying depth scenes. More importantly, our approach is an efficient CPU-based solution that enables undistorting RS videos in real-time (40fps for 480p). We evaluate our approach across a broad range of cameras and video sequences, including fast motion, dynamic scenes, and non-perspective lenses, demonstrating the superiority of our proposed approach over state-of-the-art methods in both effectiveness and efficiency. We also evaluated the ability of the RSC results to serve for downstream 3D analysis, such as visual odometry and structure-from-motion, which verifies preference for the output of our algorithm over other existing RSC methods.

摘要

本文探讨了未校准视频中的卷帘快门校正(RSC)问题。现有工作通过将相机运动和深度作为中间产物进行显式计算,随后进行运动补偿,来消除卷帘快门(RS)失真。相比之下,我们首先表明,通过重新缩放每个失真像素的光流,可以将其隐式校正回相应的全局快门(GS)投影。这种逐点RSC在透视和非透视情况下都是可行的,无需预先了解所使用的相机。此外,它允许一个逐像素变化的直接RS校正(DRSC)框架,该框架可以处理由各种源(如相机运动、移动物体,甚至深度变化很大的场景)引起的局部变化失真。更重要的是,我们的方法是一种基于CPU的高效解决方案,能够实时(480p分辨率下40fps)对RS视频进行去失真处理。我们在广泛的相机和视频序列中评估了我们的方法,包括快速运动、动态场景和非透视镜头,证明了我们提出的方法在有效性和效率方面优于现有方法。我们还评估了RSC结果用于下游3D分析(如视觉里程计和运动结构)的能力,这验证了相对于其他现有RSC方法,我们算法输出的优势。

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