Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00347821" target="_blank" >RIV/68407700:21730/20:00347821 - isvavai.cz</a>
Result on the web
<a href="https://www.springerprofessional.de/en/efficient-neighbourhood-consensus-networks-via-submanifold-spars/18555516" target="_blank" >https://www.springerprofessional.de/en/efficient-neighbourhood-consensus-networks-via-submanifold-spars/18555516</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58545-7_35" target="_blank" >10.1007/978-3-030-58545-7_35</a>

Result language
angličtina
Original language name
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
Original language description
In this work we target the problem of estimating accurately localized correspondences between a pair of images. We adopt the recent Neighbourhood Consensus Networks that have demonstrated promising performance for difficult correspondence problems and propose modifications to overcome their main limitations: large memory consumption, large inference time and poorly localized correspondences. Our proposed modifications can reduce the memory footprint and execution time more than 10x, with equivalent results. This is achieved by sparsifying the correlation tensor containing tentative matches, and its subsequent processing with a 4D CNN using submanifold sparse convolutions. localization accuracy is significantly improved by processing the input images in higher resolution, which is possible due to the reduced memory footprint, and by a novel two-stage correspondence relocalization module. The proposed Sparse-NCNet method obtains state-of-the-art results on the HPatches Sequences and InLoc visual localization benchmarks, and competitive results on the Aachen Day-Night benchmark.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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