Abstract
Learning-based optical flow estimation has been dominated with the pipeline of cost volume with convolutions for flow regression, which is inherently limited to local correlations and thus is hard to address the long-standing challenge of large displacements. To alleviate this, the state-of-the-art method, i.e., RAFT, gradually improves the quality of its predictions by producing a sequence of flow updates via a large number of iterative refinements, achieving remarkable performance but slowing down the inference speed. To enable both high accuracy and efficiency optical flow estimation, we completely revamp the dominating flow regression pipeline by reformulating optical flow as a global matching problem. Specifically, we propose a GMFlow framework, which consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. Moreover, we further introduce a refinement step that reuses GMFlow at higher-resolutions for residual flow prediction. Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark, while using only one refinement and running faster, offering new possibilities for efficient and accurate optical flow estimation. Code will be available at this https URL.
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URL
https://arxiv.org/abs/2111.13680
