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Localize Me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

G. Lu,Yan Yan,Li Ren,Jingkuan Song,N. Sebe,C. Kambhamettu

Published 2015 in IEEE International Conference on Computer Vision

ABSTRACT

Image-based localization is an essential complement to GPS localization. Current image-based localization methods are based on either 2D-to-3D or 3D-to-2D to find the correspondences, which ignore the real scene geometric attributes. The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework. Firstly, the use of a 3D model as the query enables us to efficiently select location candidates. Furthermore, the reconstruction of 3D model exploits the correlations among different images, based on the fact that images captured from different views for SfM share information through matching features. By exploring shared information (matching features) across multiple related tasks (images of the same scene captured from different views), the visual feature's view-invariance property can be improved in order to get to a higher point retrieval accuracy. More specifically, we use multi-task point retrieval framework to explore the relationship between descriptors and the 3D points, which extracts the discriminant points for more accurate 3D-to-3D correspondences retrieval. We further apply multi-task learning (MTL) retrieval approach on thermal images to prove that our MTL retrieval framework also provides superior performance for the thermal domain. This application is exceptionally helpful to cope with the localization problem in an environment with limited light sources.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    IEEE International Conference on Computer Vision

  • Publication date

    2015-12-01

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    DOI 10.1109/ICCV.2015.280
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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