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STC-Flow: Spatio-temporal context-aware optical flow estimation
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DOI：10.1016/j.image.2021.116441.png)
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摘要
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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.
Keyword:
Spatio-temporal network
Context modeling
Optical flow estimation
Deep learning
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