RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

Lahav Lipson, Zachary Teed, Jia Deng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-227
Number of pages10
ISBN (Electronic)9781665426886
DOIs
StatePublished - 2021
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: Dec 1 2021Dec 3 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021

Conference

Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period12/1/2112/3/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Keywords

  • Deep
  • GRU
  • Matching
  • Recurrent
  • Stereo

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