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Neural-Guided RANSAC for estimating epipolar geometry [data]

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Personen und Körperschaften: Brachmann, Eric (VerfasserIn)
Titel: Neural-Guided RANSAC for estimating epipolar geometry [data]/ Eric Brachmann
Format: OnlineResource Computerdaten Datenbank
Sprache: Englisch
veröffentlicht:
Heidelberg Universität 2020-09-07
Schlagwörter:
Quelle: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
Forschungsdaten zu: Brachmann, Eric, 1987 - : Neural-Guided RANSAC
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contents Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac
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spelling Brachmann, Eric 1987- VerfasserIn (DE-588)1179206088 (DE-627)1066600457 (DE-576)518117634 aut, Neural-Guided RANSAC for estimating epipolar geometry [data] Eric Brachmann, Heidelberg Universität 2020-09-07, 1 Online-Ressource (1 File), Text txt rdacontent, Computerdaten cod rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Production date: 2019-03-31, Gesehen am 14.09.2020, Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac, Forschungsdaten (DE-588)1098579690 (DE-627)857755366 (DE-576)469182156 gnd-content, Datenbank (DE-588)4011119-2 (DE-627)106354256 (DE-576)208891943 gnd-content, Forschungsdaten zu Brachmann, Eric, 1987 - Neural-Guided RANSAC 2019 (DE-627)1731809948, https://doi.org/10.11588/data/PCGYET Verlag Resolving-System kostenfrei Volltext, https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/PCGYET Verlag kostenfrei Volltext, https://doi.org/10.11588/data/PCGYET LFER, https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/PCGYET LFER, LFER 2020-11-11T09:12:06Z
spellingShingle Brachmann, Eric, Neural-Guided RANSAC for estimating epipolar geometry [data], Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac, Forschungsdaten, Datenbank
title Neural-Guided RANSAC for estimating epipolar geometry [data]
title_auth Neural-Guided RANSAC for estimating epipolar geometry [data]
title_full Neural-Guided RANSAC for estimating epipolar geometry [data] Eric Brachmann
title_fullStr Neural-Guided RANSAC for estimating epipolar geometry [data] Eric Brachmann
title_full_unstemmed Neural-Guided RANSAC for estimating epipolar geometry [data] Eric Brachmann
title_short Neural-Guided RANSAC for estimating epipolar geometry [data]
title_sort neural guided ransac for estimating epipolar geometry data
topic Forschungsdaten, Datenbank
topic_facet Forschungsdaten, Datenbank
url https://doi.org/10.11588/data/PCGYET, https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/PCGYET