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
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Dokumenttyp: | Datenbank Forschungsdaten |
| Sprache: | Englisch |
| Veröffentlicht: |
Heidelberg
Universität
2020-09-07
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| DOI: | 10.11588/data/PCGYET |
| Schlagworte: | |
| Online-Zugang: | 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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| Verfasserangaben: | Eric Brachmann |
MARC
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| 520 | |a 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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