Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Open accessAbstract
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.
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- Authors
- Sattler, T., Maddern, W., Toft, C., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., Sivic, J., Kahl, F., & Pajdla, T.
- Affiliations
- ETH Zurich (CH)
- University of Oxford (GB)
- Chalmers University of Technology (SE)
- Tokyo Institute of Technology (JP)
- Lund University (SE)
- ORCID
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8601-8610
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2740418457
- Cited by
- 699
- References
- 74
- Citations by year
- 2026: 40 · 2025: 74 · 2024: 108 · 2023: 101 · 2022: 93 · 2021: 105 · 2020: 102 · 2019: 58 · 2018: 18
- FWCI
- 122.82
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Canadian Institute for Advanced Research
- Stiftelsen för Strategisk Forskning
- Vetenskapsrådet (2016-04445)
- Horizon 2020 Framework Programme
- Engineering and Physical Sciences Research Council (EP/M019918/1, H2020)
- Japan Society for the Promotion of Science (15H05313, 15003)
- European Regional Development Fund (02.1.01/0.0/0.0, H2020, CZ.02, CZ.02.1.01, CZ.02.1.01/0.0/0.0)
- FP7 Ideas: European Research Council (336845)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- visual localization, outdoor localization, benchmark dataset, long-term localization, local features, autonomous vehicles, augmented reality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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