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Conference Paper: CamNet: Coarse-to-fine retrieval for camera re-localization

TitleCamNet: Coarse-to-fine retrieval for camera re-localization
Authors
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000149
Citation
Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October - 2 November 2019, p. 2871-2880 How to Cite?
AbstractCamera re-localization is an important but challenging task in applications like robotics and autonomous driving. Recently, retrieval-based methods have been considered as a promising direction as they can be easily generalized to novel scenes. Despite significant progress has been made, we observe that the performance bottleneck of previous methods actually lies in the retrieval module. These methods use the same features for both retrieval and relative pose regression tasks which have potential conflicts in learning. To this end, here we present a coarse-to-fine retrieval-based deep learning framework, which includes three steps, i.e., image-based coarse retrieval, pose-based fine retrieval and precise relative pose regression. With our carefully designed retrieval module, the relative pose regression task can be surprisingly simpler. We design novel retrieval losses with batch hard sampling criterion and two-stage retrieval to locate samples that adapt to the relative pose regression task. Extensive experiments show that our model (CamNet) outperforms the state-of-the-art methods by a large margin on both indoor and outdoor datasets.
Persistent Identifierhttp://hdl.handle.net/10722/284157
ISSN

 

DC FieldValueLanguage
dc.contributor.authorDing, M-
dc.contributor.authorWang, Z-
dc.contributor.authorSun, J-
dc.contributor.authorShi, J-
dc.contributor.authorLuo, P-
dc.date.accessioned2020-07-20T05:56:32Z-
dc.date.available2020-07-20T05:56:32Z-
dc.date.issued2019-
dc.identifier.citationProceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October - 2 November 2019, p. 2871-2880-
dc.identifier.issn1550-5499-
dc.identifier.urihttp://hdl.handle.net/10722/284157-
dc.description.abstractCamera re-localization is an important but challenging task in applications like robotics and autonomous driving. Recently, retrieval-based methods have been considered as a promising direction as they can be easily generalized to novel scenes. Despite significant progress has been made, we observe that the performance bottleneck of previous methods actually lies in the retrieval module. These methods use the same features for both retrieval and relative pose regression tasks which have potential conflicts in learning. To this end, here we present a coarse-to-fine retrieval-based deep learning framework, which includes three steps, i.e., image-based coarse retrieval, pose-based fine retrieval and precise relative pose regression. With our carefully designed retrieval module, the relative pose regression task can be surprisingly simpler. We design novel retrieval losses with batch hard sampling criterion and two-stage retrieval to locate samples that adapt to the relative pose regression task. Extensive experiments show that our model (CamNet) outperforms the state-of-the-art methods by a large margin on both indoor and outdoor datasets.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000149-
dc.relation.ispartofIEEE International Conference on Computer Vision (ICCV) Proceedings-
dc.rightsIEEE International Conference on Computer Vision (ICCV) Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleCamNet: Coarse-to-fine retrieval for camera re-localization-
dc.typeConference_Paper-
dc.identifier.emailLuo, P: pluo@hku.hk-
dc.identifier.authorityLuo, P=rp02575-
dc.identifier.doi10.1109/ICCV.2019.00296-
dc.identifier.scopuseid_2-s2.0-85081893709-
dc.identifier.hkuros311017-
dc.identifier.spage2871-
dc.identifier.epage2880-
dc.publisher.placeUnited States-

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