AtLoc: Attention Guided Camera Localization

Bing Wang, Changhao Chen, Chris Xiaoxuan Lu, Peijun Zhao, Niki Trigoni, Andrew Markham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.
Original languageEnglish
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence 2020
PublisherAAAI Press
Pages10393-10401
Number of pages9
ISBN (Electronic)978-1-57735-835-0
DOIs
Publication statusPublished - 3 Apr 2020
Event34th AAAI Conference on Artificial Intelligence - New York, United States
Duration: 7 Feb 202012 Feb 2020
Conference number: 34
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number6
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference34th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2020
CountryUnited States
CityNew York
Period7/02/2012/02/20
Internet address

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