Guiding Monocular Depth Estimation Using Depth-Attention Volume
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00346894" target="_blank" >RIV/68407700:21230/20:00346894 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-58574-7_35" target="_blank" >https://doi.org/10.1007/978-3-030-58574-7_35</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58574-7_35" target="_blank" >10.1007/978-3-030-58574-7_35</a>
Alternative languages
Result language
angličtina
Original language name
Guiding Monocular Depth Estimation Using Depth-Attention Volume
Original language description
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks. In this paper, we propose guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments. This is achieved by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV). Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet, show that our method achieves state-of-the-art depth estimation results while using only a fraction of the number of parameters needed by the competing methods. Code is available at: https://github.com/HuynhLam/DAV.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision - ECCV 2020, Part XXVI
ISBN
978-3-030-58573-0
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
17
Pages from-to
581-597
Publisher name
Springer
Place of publication
Cham
Event location
Glasgow
Event date
Aug 23, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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