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arXiv · 2109.07547

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

Lahav Lipson·Zachary Teed·Jia Deng
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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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Lahav Lipson, Zachary Teed, Jia Deng. 2021-09-15. RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. https://arxiv.org/abs/2109.07547

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