AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual Localisation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910856216838144 |
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| author | Liu, Changkun Jiao, Jianhao Huang, Huajian Ma, Zhengyang Kanoulas, Dimitrios Braud, Tristan |
| author_facet | Liu, Changkun Jiao, Jianhao Huang, Huajian Ma, Zhengyang Kanoulas, Dimitrios Braud, Tristan |
| contents | State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-$k$ most similar images from a reference database. While increasing $k$ improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper investigates the relationship between global and local descriptors, showing that greater similarity between the global descriptors of query and database images increases the proportion of feature matches. Low similarity queries significantly benefit from increasing $k$, while high similarity queries rapidly experience diminishing returns. Building on these observations, we propose an adaptive strategy that adjusts $k$ based on the similarity between the query's global descriptor and those in the database, effectively mitigating the feature-matching bottleneck. Our approach optimizes processing time without sacrificing accuracy. Experiments on three indoor and outdoor datasets show that AIR-HLoc reduces feature matching time by up to 30\%, while preserving state-of-the-art accuracy. The results demonstrate that AIR-HLoc facilitates a latency-sensitive localisation system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual Localisation Liu, Changkun Jiao, Jianhao Huang, Huajian Ma, Zhengyang Kanoulas, Dimitrios Braud, Tristan Computer Vision and Pattern Recognition State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-$k$ most similar images from a reference database. While increasing $k$ improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper investigates the relationship between global and local descriptors, showing that greater similarity between the global descriptors of query and database images increases the proportion of feature matches. Low similarity queries significantly benefit from increasing $k$, while high similarity queries rapidly experience diminishing returns. Building on these observations, we propose an adaptive strategy that adjusts $k$ based on the similarity between the query's global descriptor and those in the database, effectively mitigating the feature-matching bottleneck. Our approach optimizes processing time without sacrificing accuracy. Experiments on three indoor and outdoor datasets show that AIR-HLoc reduces feature matching time by up to 30\%, while preserving state-of-the-art accuracy. The results demonstrate that AIR-HLoc facilitates a latency-sensitive localisation system. |
| title | AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual Localisation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.18281 |