We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gmberton/VPR-methods-evaluation | canonical | 1 of 4 |
| gmberton/megaloc | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| denormalize | Ran | gmberton/VPR-methods-evaluation/vpr_models/utils.py code served (permissive licence) · get_code("10c34609af1437f3") |
| log_otp_solver | Ran | gmberton/megaloc/megaloc_model.py code served (permissive licence) · get_code("cc0d6b2496e86c33") |
| get_boq | Not yet run | gmberton/VPR-methods-evaluation/vpr_models/boq.py code served (permissive licence) · get_code("15f6c692126a38d8") |
| get_dino_mix | Not yet run | gmberton/VPR-methods-evaluation/vpr_models/dinomix.py code served (permissive licence) · get_code("846fe56e1c06a617") |
| get_matching_probs | Not yet run | gmberton/megaloc/megaloc_model.py code served (permissive licence) · get_code("6d34ff3423316bc7") |
| get_qaa | Not yet run | gmberton/VPR-methods-evaluation/vpr_models/qaa.py code served (permissive licence) · get_code("bd8f3a7438258b2b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However, existing solutions are built to specifically work for one of these tasks, and are known to fail when the requirements slightly change or when they meet out-of-distribution data. In this paper we combine a variety of existing methods, training techniques, and datasets to train a retrieval model, called MegaLoc, that is performant on multiple tasks. We find that MegaLoc (1) achieves state of the art on a large number of Visual Place Recognition datasets, (2) impressive results on common Landmark Retrieval datasets, and (3) sets a new state of the art for Visual Localization on the LaMAR datasets, where we only changed the retrieval method to the existing localization pipeline. The code for MegaLoc is available at https://github.com/gmberton/MegaLoc
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.17237")
get_code_for_paper("2502.17237")
have("2502.17237")
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