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Is Geometry Enough for Matching in Visual Localization?

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

In this paper, we propose to go beyond the well-established approach to vision-based localization that relies on visual descriptor matching between a query image and a 3D point cloud. While matching keypoints via visual descriptors makes localization highly accurate, it has significant storage demands, raises privacy concerns and requires update to the descriptors in the long-term. To elegantly address those practical challenges for large-scale localization, we present GoMatch, an alternative to visual-based matching that solely relies on geometric information for matching image keypoints to maps, represented as sets of bearing vectors. Our novel bearing vectors representation of 3D points, significantly relieves the cross-modal challenge in geometric-based matching that prevented prior work to tackle localization in a realistic environment. With additional careful architecture design, GoMatch improves over prior geometric-based matching work with a reduction of (\(10.67\,\text {m}, 95.7^{\circ }\)) and (\(1.43\,\text {m}\), \(34.7^{\circ }\)) in average median pose errors on Cambridge Landmarks and 7-Scenes, while requiring as little as \(1.5/1.7\%\) of storage capacity in comparison to the best visual-based matching methods. This confirms its potential and feasibility for real-world localization and opens the door to future efforts in advancing city-scale visual localization methods that do not require storing visual descriptors.

Q. Zhou and S. Agostinho—Equal contribution.

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Notes

  1. 1.

    Storage as in non-volatile preservation of data, in contrast to volatile memory.

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Acknowledgments

This research was partially funded by the Humboldt Foundation through the Sofja Kovalevskaya Award.

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Authors and Affiliations

  1. Technical University of Munich, Munich, Germany

    Qunjie Zhou, Aljoša Ošep & Laura Leal-Taixé

  2. Universidade de Lisboa, Lisbon, Portugal

    Sérgio Agostinho

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  1. Qunjie Zhou
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  2. Sérgio Agostinho
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  4. Laura Leal-Taixé
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Correspondence to Qunjie Zhou .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhou, Q., Agostinho, S., Ošep, A., Leal-Taixé, L. (2022). Is Geometry Enough for Matching in Visual Localization?. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_24

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