Occlusions pose a significant challenge to optical flow algorithms that even rely on global evidences. We consider an occluded point to be one that is imaged in the reference frame but not in the next. Estimating the motion of these points is extremely difficult, particularly in the two-frame setting. Previous work only used the current frame as the only input, which could not guarantee providing correct global reference information for occluded points, and had problems such as long calculation time and poor accuracy in predicting optical flow at occluded points. To enable both high accuracy and efficiency, We fully mine and utilize the spatiotemporal information provided by the frame pair, design a loopback judgment algorithm to ensure that correct global reference information is obtained, mine multiple necessary global information, and design an efficient refinement module that fuses these global information. Specifically, we propose a YOIO framework, which consists of three main components: an initial flow estimator, a multiple global information extraction module, and a unified refinement module. We demonstrate that optical flow estimates in the occluded regions can be significantly improved in only one iteration without damaging the performance in non-occluded regions. Compared with GMA, the optical flow prediction accuracy of this method in the occluded area is improved by more than 10%, and the occ_out area exceeds 15%, while the calculation time is 27% shorter. This approach, running up to 18.9fps with 436*1024 image resolution, obtains new state-of-the-art results on the challenging Sintel dataset among all published and unpublished approaches that can run in real-time, suggesting a new paradigm for accurate and efficient optical flow estimation.

翻译：遮挡问题对即使依赖全局证据的光流算法也是一项重大挑战。我们定义被遮挡点为在参考帧中成像但在下一帧中未出现的点。估计这些点的运动极为困难，尤其是在双帧设定下。现有工作仅将当前帧作为唯一输入，无法保证为被遮挡点提供正确的全局参考信息，且存在计算时间长、被遮挡点光流预测精度低等问题。为实现高精度与高效率，我们充分挖掘并利用帧对提供的时空信息，设计循环验证算法确保获取正确全局参考信息，挖掘多项必要全局信息，并设计了融合这些全局信息的高效精化模块。具体而言，我们提出YOIO框架，包含三个核心组件：初始光流估计器、多项全局信息提取模块及统一精化模块。实验表明，仅需单次迭代即可显著提升遮挡区域的光流估计质量，同时不损害非遮挡区域的性能。与GMA相比，本方法在遮挡区域的光流预测精度提升超过10%，在occ_out区域提升超过15%，且计算时间缩短27%。该方法以436×1024图像分辨率运行时可达18.9fps，在具有挑战性的Sintel数据集上，在所有已发表和未发表的实时运行方法中取得了最新最优结果，为精确高效的光流估计提供了新范式。