  We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds
multi-scale 4D correlation volumes for all pairs of pixels, and iteratively
updates a flow field through a recurrent unit that performs lookups on the
correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT
achieves an F1-all error of 5.10%, a 16% error reduction from the best
published result (6.10%). On Sintel (final pass), RAFT obtains an
end-point-error of 2.855 pixels, a 30% error reduction from the best published
result (4.098 pixels). In addition, RAFT has strong cross-dataset
generalization as well as high efficiency in inference time, training speed,
and parameter count. Code is available at https://github.com/princeton-vl/RAFT.
