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