The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
Open accessAbstract
Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization.
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Cited by
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Liu, Z., Zhou, S., Suo, C., Yin, P., Chen, W., Wang, H., Li, H., & Liu, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00292 · Cited by 325
Grounding Image Matching in 3D with MASt3R
Leroy, V., Cabon, Y., & Revaud, J. (2024) · arXiv · doi:10.48550/arxiv.2406.09756 · Cited by 309
COTR: Correspondence Transformer for Matching Across Images
Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., & Yi, K. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00615 · Cited by 282
Lim, H., Hwang, S., & Myung, H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3061363 · Cited by 268
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175
“feature network (HF-Net) [180], a compressed model trained”
BackgroundMethodInfluential
Metadata
- Authors
- Sarlin, P.-E., Cadena, C., Siegwart, R., & Dymczyk, M.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 12708-12717
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2905255369
- Cited by
- 1,044
- References
- 58
- FWCI
- 151.57
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sarlin, P.-E., Cadena, C., Siegwart, R., & Dymczyk, M. (2019). From Coarse to Fine: Robust Hierarchical Localization at Large Scale. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01300
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