arXiv Open Access 2021

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

Lahav Lipson Zachary Teed Jia Deng

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Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (3)

Lahav Lipson

Zachary Teed

Jia Deng

Format Sitasi

APA MLA BibTeX

Lipson, L., Teed, Z., Deng, J. (2021). RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. https://arxiv.org/abs/2109.07547

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓