UnLoc: Leveraging Depth Uncertainties for Floorplan Localization
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911516716957696 |
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| author | Wüest, Matthias Engelmann, Francis Miksik, Ondrej Pollefeys, Marc Barath, Daniel |
| author_facet | Wüest, Matthias Engelmann, Francis Miksik, Ondrej Pollefeys, Marc Barath, Daniel |
| contents | We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of recent methods, such as the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions. By leveraging off-the-shelf pre-trained monocular depth models, we eliminate the need to rely on per-environment-trained depth networks, enhancing generalization to unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements over existing methods in terms of accuracy and robustness. Notably, we achieve $2.7$ times higher localization recall on long sequences (100 frames) and $42.2$ times higher on short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_11301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UnLoc: Leveraging Depth Uncertainties for Floorplan Localization Wüest, Matthias Engelmann, Francis Miksik, Ondrej Pollefeys, Marc Barath, Daniel Computer Vision and Pattern Recognition We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of recent methods, such as the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions. By leveraging off-the-shelf pre-trained monocular depth models, we eliminate the need to rely on per-environment-trained depth networks, enhancing generalization to unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements over existing methods in terms of accuracy and robustness. Notably, we achieve $2.7$ times higher localization recall on long sequences (100 frames) and $42.2$ times higher on short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset. |
| title | UnLoc: Leveraging Depth Uncertainties for Floorplan Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.11301 |