Result details - ETH3D

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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

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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
	4.24	4.65	5.90	11.00	4.41	8.70	3.19	5.19	2.19	2.86	1.78	6.28	2.81	0.13	4.51	2.15	2.50	1.01	3.64	0.81	3.11	2.22	0.64	3.28	7.94	5.77	8.62	9.05

SLAM results

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	Method	Info
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