Result details - ETH3D

Submitted anonymously.

Submission data

Full name	fully-convolutional densely-connected neural network
Description	We propose a novel lightweight network for stereo estimation. The method uses densely connected layer structures to learn expressive features without the need of fully-connected layers or 3D convolutions. This leads to a network structure with only 0.37M parameters while still having competitive results. The post-processing consists of filtering, a consistency check and hole filling. This paper has been accepted to the ICPR 2020 conference in Milan. Second version. Slight changes over the first version due to better hyperparameter search. For more details check out the github page.
Programming language(s)	python
Hardware	GeForce 2080 GTX
Submission creation date	29 Aug, 2024
Last edited	29 Aug, 2024

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	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
	6.60	0.46	3.23	0.56	0.58	8.28	1.46	9.35	7.29	19.57	23.13	23.87	14.94	10.69	7.88	0.00	0.01	0.00	0.00	0.03	0.63

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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