Result details - ETH3D

Submitted by Dominik Hirner.

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 which will be held on the 10-15 January 2021. Therefore this work has not yet been presented
Parameters	\eta = 6 \times 10^{-6}
Programming language(s)	python3, pytorch
Hardware	GeForce 2080 GTX
Source code or download URL	https://github.com/thedodo/fc-dcnn2
Submission creation date	25 Mar, 2020
Last edited	9 Nov, 2020

High-res multi-view results

Info	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.
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Low-res many-view results

Info	all	low-res many-view	indoor	outdoor	delivery area	electro	forest	playground	terrains
No results yet.

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
	11.29	1.01	5.50	2.25	1.65	18.23	5.36	18.99	20.65	28.95	34.83	33.83	23.18	17.04	13.37	0.17	0.01	0.02	0.02	0.10	0.70

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
No results yet.