  Recently, the dense correlation volume method achieves state-of-the-art
performance in optical flow. However, the correlation volume computation
requires a lot of memory, which makes prediction difficult on high-resolution
images. In this paper, we propose a novel Patchmatch-based framework to work on
high-resolution optical flow estimation. Specifically, we introduce the first
end-to-end Patchmatch based deep learning optical flow. It can get
high-precision results with lower memory benefiting from propagation and local
search of Patchmatch. Furthermore, a new inverse propagation is proposed to
decouple the complex operations of propagation, which can significantly reduce
calculations in multiple iterations. At the time of submission, our method
ranks first on all the metrics on the popular KITTI2015 benchmark, and ranks
second on EPE on the Sintel clean benchmark among published optical flow
methods. Experiment shows our method has a strong cross-dataset generalization
ability that the F1-all achieves 13.73%, reducing 21% from the best published
result 17.4% on KITTI2015. What's more, our method shows a good details
preserving result on the high-resolution dataset DAVIS and consumes 2x less
memory than RAFT.
