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Optical flow estimation is an important computer vision task, which aims atestimating the dense correspondences between two frames. RAFT (Recurrent AllPairs Field Transforms) currently represents the state-of-the-art in opticalflow estimation. It has excellent generalization ability and has obtainedoutstanding results across several benchmarks. To further improve therobustness and achieve accurate optical flow estimation, we present PRAFlow(Pyramid Recurrent All-Pairs Flow), which builds upon the pyramid networkstructure. Due to computational limitation, our proposed network structure onlyuses two pyramid layers. At each layer, the RAFT unit is used to estimate theoptical flow at the current resolution. Our model was trained on severalsimulate and real-image datasets, submitted to multiple leaderboards using thesame model and parameters, and won the 2nd place in the optical flow task ofECCV 2020 workshop: Robust Vision Challenge.

PRAFlow_RVC: Pyramid Recurrent All-Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020