Deep learning approaches to optical flow estimation have seen rapid progress over the recent years. One common trait of many networks is that they refine an initial flow estimate either through multiple stages or across the levels of a coarse-to-fine representation. While leading to more accurate results, the downside of this is an increased number of parameters. Taking inspiration from both classical energy minimization approaches as well as residual networks, we propose an iterative residual refinement (IRR) scheme based on weight sharing that can be combined with several backbone networks. It reduces the number of parameters, improves the accuracy, or even achieves both. Moreover, we show that integrating occlusion prediction and bi-directional flow estimation into our IRR scheme can further boost the accuracy. Our full network achieves state-of-the-art results for both optical flow and occlusion estimation across several standard datasets.

本文提出了一种基于迭代残差细化和权重共享的光流估计方法，它结合了经典能量最小化方法和残余网络的优点，通过整合遮挡预测和双向流估计，进一步提高了准确性。与其他方法相比，该方法显著提高了光流估计和遮挡估计的准确性，且参数数量更少。

联合光流和遮挡估计的迭代残差细化