Result details - ETH3D

Submitted by JUNHONG MIN.

Submission data

Full name	Confidence-Aware Symmetric Stereo Matching via U-Net Transformer
Description	we propose a novel deep stereo matching network a new real-world stereo dataset of cluttered objects taken with a commercially available stereo sensor. We design a U-shaped architecture with various types of attentions which more efficiently extracts global and local contexts from rectified image pairs, resulting in highly accurate disparities. Furthermore, its symmetric structure allows simultaneous estimation both left and right disparity. It can also implicitly estimate the uncertainty i.e. the confidence of estimated disparities.
Parameters	feature dimension 128: 4-level U-net for the feature extraction 3-level U-net for the refinement processing at 1536x1024 pixel scale
Publication title	Confidence-Aware Symmetric Stereo Matching via U-Net Transformer
Publication authors	Junhong Min, Youngpil Jeon
Programming language(s)	Python
Hardware	i9-13900k, Nvidia RTX 4090
Submission creation date	14 Sep, 2023
Last edited	14 Sep, 2023

High-res multi-view results

Info	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.
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Low-res many-view results

Info	all	low-res many-view	indoor	outdoor	lakeside	sand box	storage room	storage room 2	tunnel
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Low-res two-view results

Info	all	deliv. area 1l	deliv. area 1s	deliv. area 2l	deliv. area 2s	deliv. area 3l	deliv. area 3s	elect. 1l	elect. 1s	elect. 2l	elect. 2s	elect. 3l	elect. 3s	facade 1s	forest 1s	forest 2s	playg. 1l	playg. 1s	playg. 2l	playg. 2s	playg. 3l	playg. 3s	terra. 1s	terra. 2s	terra. 1l	terra. 1s	terra. 2l	terra. 2s
	0.51	0.27	0.27	0.32	0.26	0.29	0.67	0.40	0.30	0.47	0.52	0.47	0.36	0.16	0.74	0.67	0.71	0.46	0.78	0.28	1.67	1.48	0.17	0.20	0.42	0.34	0.53	0.46

SLAM results

		all	boxes	boxes dark	buddha	cables 4	cables 5	desk 1	desk 2	desk changing 2	desk dark 1	desk dark 2	desk global light changes	desk ir light	dino	drone	foreground occlusion	helmet	kidnap 2	lamp	large loop 2	large loop 3	large non loop	motion 2	motion 3	motion 4	planar 1	reflective 2	scale change	table 1	table 2	table 5	table 6	table global light changes	table local light changes	table scene	trashbin
	Method	Info
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