  We introduce RAFT-Stereo, a new deep architecture for rectified stereo based
on the optical flow network RAFT. We introduce multi-level convolutional GRUs,
which more efficiently propagate information across the image. A modified
version of RAFT-Stereo can perform accurate real-time inference. RAFT-stereo
ranks first on the Middlebury leaderboard, outperforming the next best method
on 1px error by 29% and outperforms all published work on the ETH3D two-view
stereo benchmark. Code is available at
https://github.com/princeton-vl/RAFT-Stereo.
