@inproceedings{880de7812f4e499d815ff23a7761ceaa,
title = "RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching",
abstract = "We introduce RAFT-Stereo,a new deep architecture for rectified stereo based on the optical flow network RAFT [35]. 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.",
keywords = "Deep, GRU, Matching, Recurrent, Stereo",
author = "Lahav Lipson and Zachary Teed and Jia Deng",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th International Conference on 3D Vision, 3DV 2021 ; Conference date: 01-12-2021 Through 03-12-2021",
year = "2021",
doi = "10.1109/3DV53792.2021.00032",
language = "English (US)",
series = "Proceedings - 2021 International Conference on 3D Vision, 3DV 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "218--227",
booktitle = "Proceedings - 2021 International Conference on 3D Vision, 3DV 2021",
address = "United States",
}