Result details - ETH3D

Submitted by Zachary Teed.

Submission data

Full name	RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
Description	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.
Publication title	RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
Publication authors	Lahav Lipson, Zachary Teed, and Jia Deng
Publication venue	3DV
Publication URL	https://arxiv.org/abs/2109.07547
Programming language(s)	Pytthon
Hardware	RTX 6000
Source code or download URL	https://github.com/princeton-vl/RAFT-Stereo
Submission creation date	5 Nov, 2020
Last edited	4 Oct, 2021

High-res multi-view results

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Low-res many-view results

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Low-res two-view results

Info	all	lakes. 1l	lakes. 1s	sand box 1l	sand box 1s	stora. room 1l	stora. room 1s	stora. room 2l	stora. room 2s	stora. room 2 1l	stora. room 2 1s	stora. room 2 2l	stora. room 2 2s	stora. room 3l	stora. room 3s	tunnel 1l	tunnel 1s	tunnel 2l	tunnel 2s	tunnel 3l	tunnel 3s
	0.81	0.96	0.67	0.95	0.67	0.95	0.68	0.95	0.68	0.95	0.68	0.96	0.68	0.96	0.68	0.94	0.68	0.94	0.68	0.94	0.68

SLAM results

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	Method	Info
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