  We introduce SEA-RAFT, a more simple, efficient, and accurate RAFT for
optical flow. Compared with RAFT, SEA-RAFT is trained with a new loss (mixture
of Laplace). It directly regresses an initial flow for faster convergence in
iterative refinements and introduces rigid-motion pre-training to improve
generalization. SEA-RAFT achieves state-of-the-art accuracy on the Spring
benchmark with a 3.69 endpoint-error (EPE) and a 0.36 1-pixel outlier rate
(1px), representing 22.9% and 17.8% error reduction from best published
results. In addition, SEA-RAFT obtains the best cross-dataset generalization on
KITTI and Spring. With its high efficiency, SEA-RAFT operates at least 2.3x
faster than existing methods while maintaining competitive performance. The
code is publicly available at https://github.com/princeton-vl/SEA-RAFT.
