On Model-Free Re-Ranking for Visual Place Recognition With Deep Learned Local Features Article Swipe
Tomáš Pivoňka
,
Libor Přeučil
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tiv.2024.3404564
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tiv.2024.3404564
Re-ranking is the second stage of a visual place recognition task, in which\nthe system chooses the best-matching images from a pre-selected subset of\ncandidates. Model-free approaches compute the image pair similarity based on a\nspatial comparison of corresponding local visual features, eliminating the need\nfor computationally expensive estimation of a model describing transformation\nbetween images. The article focuses on model-free re-ranking based on standard\nlocal visual features and their applicability in long-term autonomy systems. It\nintroduces three new model-free re-ranking methods that were designed primarily\nfor deep-learned local visual features. These features evince high robustness\nto various appearance changes, which stands as a crucial property for use with\nlong-term autonomy systems. All the introduced methods were employed in a new\nvisual place recognition system together with the D2-net feature detector\n(Dusmanu, 2019) and experimentally tested with diverse, challenging public\ndatasets. The obtained results are on par with current state-of-the-art\nmethods, affirming that model-free approaches are a viable and worthwhile path\nfor long-term visual place recognition.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tiv.2024.3404564
- OA Status
- green
- Cited By
- 3
- References
- 33
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4398249504Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tiv.2024.3404564Digital Object Identifier
- Title
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On Model-Free Re-Ranking for Visual Place Recognition With Deep Learned Local FeaturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-23Full publication date if available
- Authors
-
Tomáš Pivoňka, Libor PřeučilList of authors in order
- Landing page
-
https://doi.org/10.1109/tiv.2024.3404564Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.18573Direct OA link when available
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Computer science, Artificial intelligence, Ranking (information retrieval), Robustness (evolution), Visualization, Pattern recognition (psychology), Machine learning, Term (time), Computer vision, Physics, Quantum mechanics, Chemistry, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.together | 114 |
| abstract_inverted_index.affirming | 137 |
| abstract_inverted_index.expensive | 43 |
| abstract_inverted_index.features, | 38 |
| abstract_inverted_index.features. | 82 |
| abstract_inverted_index.long-term | 66, 147 |
| abstract_inverted_index.need\nfor | 41 |
| abstract_inverted_index.path\nfor | 146 |
| abstract_inverted_index.Model-free | 23 |
| abstract_inverted_index.Re-ranking | 0 |
| abstract_inverted_index.a\nspatial | 32 |
| abstract_inverted_index.appearance | 89 |
| abstract_inverted_index.approaches | 24, 140 |
| abstract_inverted_index.comparison | 33 |
| abstract_inverted_index.describing | 48 |
| abstract_inverted_index.estimation | 44 |
| abstract_inverted_index.introduced | 104 |
| abstract_inverted_index.model-free | 55, 72, 139 |
| abstract_inverted_index.re-ranking | 56, 73 |
| abstract_inverted_index.similarity | 29 |
| abstract_inverted_index.which\nthe | 12 |
| abstract_inverted_index.worthwhile | 145 |
| abstract_inverted_index.challenging | 126 |
| abstract_inverted_index.eliminating | 39 |
| abstract_inverted_index.new\nvisual | 110 |
| abstract_inverted_index.recognition | 9, 112 |
| abstract_inverted_index.deep-learned | 79 |
| abstract_inverted_index.pre-selected | 20 |
| abstract_inverted_index.applicability | 64 |
| abstract_inverted_index.best-matching | 16 |
| abstract_inverted_index.corresponding | 35 |
| abstract_inverted_index.It\nintroduces | 69 |
| abstract_inverted_index.experimentally | 122 |
| abstract_inverted_index.primarily\nfor | 78 |
| abstract_inverted_index.recognition.\n | 150 |
| abstract_inverted_index.robustness\nto | 87 |
| abstract_inverted_index.computationally | 42 |
| abstract_inverted_index.of\ncandidates. | 22 |
| abstract_inverted_index.standard\nlocal | 59 |
| abstract_inverted_index.with\nlong-term | 99 |
| abstract_inverted_index.public\ndatasets. | 127 |
| abstract_inverted_index.detector\n(Dusmanu, | 119 |
| abstract_inverted_index.transformation\nbetween | 49 |
| abstract_inverted_index.state-of-the-art\nmethods, | 136 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.90576609 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |