RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

• URL: http://arxiv.org/abs/2109.07547v1
• Date: Wed, 15 Sep 2021 19:27:31 GMT
• Title: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
• Authors: Lahav Lipson, Zachary Teed, Jia Deng
• Abstract summary: 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.
• Score: 60.44903340167672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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