Submitted by Dominik Hirner.

Submission data

Full namefully-convolutional densely-connected neural network
DescriptionWe 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
HardwareGeForce 2080 GTX
Source code or download URLhttps://github.com/thedodo/fc-dcnn2
Submission creation date25 Mar, 2020
Last edited9 Nov, 2020

High-res multi-view results



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indooroutdoorcourty.delive.electrofacadekickermeadowofficepipesplaygr.reliefrelief.terraceterrai.
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Low-res two-view results



Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
copylefttwo views11.291.015.502.251.6518.235.3618.9920.6528.9534.8333.8323.1817.0413.370.170.010.020.020.100.70

SLAM results



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