RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

摘要

We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for estimating 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 both KITTI and Sintel, with strong cross-dataset generalization and high efficiency in inference time, training speed, and parameter count.