论文标题
STC流：时空上下文感知的光流估计
STC-Flow: Spatio-temporal Context-aware Optical Flow Estimation
论文作者
论文摘要
在本文中，我们提出了一个时空上下文网络STC-Flow，以进行光流估计。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中捕获丰富的长距离依赖性来提出上下文关系探索体系结构。具体而言，STC -Flow包含三个关键上下文模块 - 锥体空间上下文模块，时间上下文相关模块和复发性残差上下文取样模块，以分别在特征提取，相关和流动重建的每个阶段建立关系。实验结果表明，所提出的方案实现了Sintel数据集和Kitti 2012/2015数据集对基于两框的方法的最先进性能。
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 - pyramidal spatial context module, temporal context correlation module and recurrent residual contextual upsampling module, to build the relationship in each stage of feature extraction, correlation, and flow reconstruction, respectively. Experimental results indicate that the proposed scheme achieves the state-of-the-art performance of two-frame based methods on the Sintel dataset and the KITTI 2012/2015 datasets.
