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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

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Bibliographic Details
Main Author: Brachmann, Eric (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2020-09-07
DOI:10.11588/data/PCGYET
Subjects:
Forschungsdaten
Datenbank
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/data/PCGYET
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/PCGYET
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Author Notes:Eric Brachmann
Description
Summary: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
Item Description:Production date: 2019-03-31
Gesehen am 14.09.2020
Physical Description:Online Resource
DOI:10.11588/data/PCGYET

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