In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature extraction and multi-level correlation, we propose a contextual relation exploration architecture by capturing rich long-range dependencies in spatial and temporal dimensions. Specifically, STC-Flow contains three key context modules, i.e., pyramidal spatial context module, temporal context correlation module and recurrent residual contextual upsampling module for the effect of feature extraction, correlation, and flow reconstruction, respectively. Experimental results demonstrate that the proposed scheme achieves the state-of-the-art performance of two-frame based methods on Sintel and KITTI datasets.

在本文中，我们提出了一种用于光流估计的时空上下文网络 STC-Flow。与以前具有局部金字塔特征提取和多级相关性的光流估计方法不同，我们通过在空间和时间维度上捕获丰富的远程依赖性，提出了一种上下文关系探索架构。具体来说，STC-Flow 包含三个关键的上下文模块，即金字塔空间上下文模块、时间上下文相关模块和循环残差上下文上采样模块，分别用于特征提取、相关和流重建的效果。实验结果表明，所提出的方案在 Sintel 和 KITTI 数据集上实现了基于两帧的方法的最先进性能。