Results for FC-DCNN
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 | lakeside | sand box | storage room | storage room 2 | tunnel |
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No results yet. |
Low-res two-view results
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 | |||
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No results yet. |