NCNet: Neighbourhood Consensus Networks for Estimating Image Correspondences
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00343642" target="_blank" >RIV/68407700:21730/22:00343642 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2020.3016711" target="_blank" >https://doi.org/10.1109/TPAMI.2020.3016711</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2020.3016711" target="_blank" >10.1109/TPAMI.2020.3016711</a>
Alternative languages
Result language
angličtina
Original language name
NCNet: Neighbourhood Consensus Networks for Estimating Image Correspondences
Original language description
We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF, TSS, InLoc and HPatches benchmarks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
44
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1020-1034
UT code for WoS article
000740006100034
EID of the result in the Scopus database
2-s2.0-85122799504