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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

RelocNet: Continuous Metric Learning Relocalisation Using Neural Nets

verfasst von : Vassileios Balntas, Shuda Li, Victor Prisacariu

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors. We introduce information from camera frusta overlaps between pairs of images to optimise our feature embedding network. Thus, the final camera pose descriptor differences represent camera pose changes. In addition, we build a pose regressor that is trained with a geometric loss to infer finer relative poses between a query and nearest neighbour images. Experiments show that our method is able to generalise in a meaningful way, and outperforms related methods across several experiments.

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Vorheriges Kapitel CornerNet: Detecting Objects as Paired Keypoints
Nächstes Kapitel The Contextual Loss for Image Transformation with Non-aligned Data
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Metadaten
Titel
RelocNet: Continuous Metric Learning Relocalisation Using Neural Nets
verfasst von
Vassileios Balntas
Shuda Li
Victor Prisacariu
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01263-2

Electronic ISBN: 978-3-030-01264-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01264-9_46