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arxiv: 2605.09418 · v1 · submitted 2026-05-10 · 💻 cs.CV · cs.RO

Recognition: no theorem link

MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition

Hanyu Zhu, Javier Civera, Wanzeng Kong, Yuhang Ming, Zhengyi Xu, Zhihao Zhan

Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:52 UTC · model grok-4.3

classification 💻 cs.CV cs.RO
keywords cross-view place recognitionmulti-modal fusionaerial-ground matchingneural ODEvector of locally aggregated queriesfoundation modelsglobal descriptor
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The pith

MAG-VLAQ uses foundation-model tokens and ODE-conditioned query aggregation to achieve much higher accuracy in aerial-ground place recognition.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops MAG-VLAQ to address the challenge of matching places seen from ground level against aerial references despite large differences in viewpoint, sensor type, and structure. It extracts dense visual tokens from images and geometric tokens from LiDAR using pre-trained foundation models, projects the tokens into one shared space, and then fuses the RGB and LiDAR information with neural ordinary differential equations. These fused states dynamically adapt the centers of locally aggregated queries so the resulting global descriptor stays close to general retrieval prototypes yet fits the current scene. Readers would care because accurate cross-view place recognition is a core requirement for robots and vehicles that must localize using mixed ground and overhead data. If the claim holds, the approach shows a practical way to turn separate foundation models into a single retrieval system that handles real modality gaps.

Core claim

The central claim is that leveraging pre-trained foundation models to extract dense visual tokens from ground and aerial images plus geometric tokens from LiDAR, projecting them into a shared embedding space, and then tightly coupling neural ODE-based RGB-LiDAR fusion with vectors of locally aggregated queries produces global descriptors that preserve globally learned retrieval prototypes while remaining responsive to scene-specific visual and geometric evidence, thereby significantly improving aerial-ground matching.

What carries the argument

ODE-conditioned VLAQ, which dynamically adapts the centers of vectors of locally aggregated queries according to the state produced by neural ordinary differential equations fusing RGB and LiDAR information.

If this is right

  • The final global descriptor preserves learned retrieval prototypes while adapting to scene-specific evidence.
  • The method achieves 61.1 Recall@1 on the KITTI360-AG satellite setting, nearly double the 34.5 of the closest prior approach.
  • Performance gains are also shown on the nuScenes-AG benchmark.
  • Pre-trained foundation models supply tokens that become aligned and fused for cross-modal retrieval without losing their general knowledge.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same ODE-driven adaptation of query centers could be tested on other cross-modal tasks such as matching images to point clouds from different platforms.
  • If the dynamic adaptation proves robust, similar mechanisms might improve single-modality place recognition when training data are limited.
  • The framework implies that continuous fusion via differential equations can make discrete token aggregation more responsive to local geometry.

Load-bearing premise

Projecting heterogeneous tokens from separate foundation models into a shared embedding space and then dynamically adapting VLAQ centers via ODE-based RGB-LiDAR fusion will produce descriptors that generalize across viewpoint and modality gaps without introducing new alignment errors or overfitting to the training scenes.

What would settle it

Evaluating MAG-VLAQ on an independent aerial-ground dataset recorded in unseen environments or with different sensor characteristics and finding that its Recall@1 improvement over the next-best method falls below 20 percent.

Figures

Figures reproduced from arXiv: 2605.09418 by Hanyu Zhu, Javier Civera, Wanzeng Kong, Yuhang Ming, Zhengyi Xu, Zhihao Zhan.

Figure 1
Figure 1. Figure 1: We propose MAG-VLAQ for multi-modal aerial-ground place recognition. Unlike prior methods whose ground descriptors may remain far from the aerial descriptor distribution, MAG-VLAQ introduces observation-dependent adaptive query aggregation to condition descriptor construction on the fused ground representation, bringing ground descriptors closer to the aerial distribution and improving SoTA’s Recall@1 from… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of MAG-VLAQ. Ground RGB and LiDAR inputs are encoded with foundation models into local tokens and fused by a multi-scale ODE module to produce a fused feature. This feature conditions VLAQ query centers, enabling observation-dependent query-residual aggregation for the ground descriptor. Aerial images are encoded and aggregated by a shared VLAQ to form database descriptors, followed by nearest-nei… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results of top-1 retrievals under all five experimental settings. For each query, we show the ground-view image, LiDAR point cloud, target aerial reference, and the top-1 retrieval results of MAG-VLAQ, AGPlace, and DC-VLAQ. Green boxes indicate correct retrievals, while red boxes indicate incorrect or failed retrievals. The number in the bottom-right corner of each retrieval result denotes the … view at source ↗
Figure 4
Figure 4. Figure 4: Attention Visualization [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
read the original abstract

Multi-modal cross-view place recognition remains a fundamental challenge in computer vision and robotics due to the severe viewpoint, modality, and spatial-structure discrepancies between ground observations and aerial references. To address this challenge, we present MAG-VLAQ, a foundation-model-enhanced query aggregation framework for multi-modal aerial-ground cross-view place recognition. Specifically, our approach leverages pre-trained foundation models to extract dense visual tokens from both ground and aerial images, as well as expressive geometric tokens from ground LiDAR observations. These heterogeneous tokens are then projected into a shared embedding space for cross-modal alignment and fusion. As our main contribution, we propose ODE-conditioned VLAQ, which tightly couples neural ordinary differential equations (ODE)-based RGB-LiDAR fusion with vectors of locally aggregated queries (VLAQ). In this design, the VLAQ query centers are dynamically adapted according to the fused multi-modal state. This mechanism allows the final global descriptor to preserve globally learned retrieval prototypes while remaining responsive to scene-specific visual and geometric evidence, significantly improving aerial-ground matching. Extensive experiments on KITTI360-AG and nuScenes-AG validate the effectiveness of our proposed MAG-VLAQ. Notably, on KITTI360-AG, our MAG-VLAQ nearly doubles the state-of-the-art performance, achieving 61.1 Recall@1 in the satellite setting, compared with 34.5 from the closest competing approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims to introduce MAG-VLAQ, a multi-modal query aggregation framework for cross-view place recognition that leverages pre-trained foundation models for token extraction from RGB and LiDAR data, projects them into a shared space, and uses ODE-conditioned VLAQ to dynamically adapt query centers for better fusion and descriptor generation. It reports substantial performance improvements on KITTI360-AG and nuScenes-AG, nearly doubling the state-of-the-art Recall@1 to 61.1 in the satellite setting.

Significance. Should the central claims hold upon verification, this work would represent a meaningful advance in multi-modal cross-view place recognition by showing how neural ODEs can be integrated with aggregated query vectors to handle modality and viewpoint gaps. The use of foundation models and the dynamic adaptation mechanism could influence future designs in visual localization for robotics. The reported performance jump indicates high potential impact if the mechanism is shown to be the causal factor.

major comments (2)
  1. [Abstract and §3.3] Abstract and §3.3 (ODE-conditioned VLAQ): The central claim that the ODE-based RGB-LiDAR fusion dynamically adapts VLAQ centers to produce descriptors that close the viewpoint/modality gap without new alignment errors is load-bearing for the reported 61.1 vs. 34.5 Recall@1 gain, yet the manuscript provides no direct supporting measurements such as pre/post-ODE alignment error, center-shift statistics, or an ablation isolating the ODE component from the foundation-model backbones.
  2. [§4] §4 (Experiments on KITTI360-AG): The headline result is presented without failure-case analysis or out-of-distribution geometry tests that would confirm the adaptation step generalizes rather than overfitting to the training scenes, undermining attribution of the doubling to the proposed mechanism.
minor comments (2)
  1. [Figure 1] The caption of the overall architecture figure should explicitly label the ODE module and the flow of VLAQ center adaptation.
  2. [§3.2] Notation for the shared embedding projection and VLAQ query centers could be introduced with a single equation in §3.2 for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the presentation of our contributions.

read point-by-point responses

  1. Referee: [Abstract and §3.3] Abstract and §3.3 (ODE-conditioned VLAQ): The central claim that the ODE-based RGB-LiDAR fusion dynamically adapts VLAQ centers to produce descriptors that close the viewpoint/modality gap without new alignment errors is load-bearing for the reported 61.1 vs. 34.5 Recall@1 gain, yet the manuscript provides no direct supporting measurements such as pre/post-ODE alignment error, center-shift statistics, or an ablation isolating the ODE component from the foundation-model backbones.

    Authors: We agree that direct measurements would provide stronger causal evidence for the ODE's role in the reported gains. The current manuscript validates overall effectiveness through end-to-end comparisons on two benchmarks, but lacks the specific pre/post-ODE alignment error, center-shift statistics, and isolated ODE ablation requested. In the revised version we will add these analyses, including quantitative center-shift distributions and alignment error reductions attributable to the ODE conditioning, to better substantiate the dynamic adaptation mechanism. revision: yes

  2. Referee: [§4] §4 (Experiments on KITTI360-AG): The headline result is presented without failure-case analysis or out-of-distribution geometry tests that would confirm the adaptation step generalizes rather than overfitting to the training scenes, undermining attribution of the doubling to the proposed mechanism.

    Authors: We acknowledge that explicit failure-case analysis and out-of-distribution tests would strengthen claims of generalization. Our evaluation already spans two datasets with differing characteristics (KITTI360-AG and nuScenes-AG), but does not include dedicated failure modes or OOD geometry experiments. We will add a new subsection with failure-case visualizations, quantitative error breakdowns, and additional OOD tests in the revised manuscript to better demonstrate that the performance improvements arise from the proposed adaptation rather than scene-specific overfitting. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation relies on external pre-trained models and empirical validation

full rationale

The paper extracts tokens from separate pre-trained foundation models, projects them into a shared space, and introduces a new ODE-based conditioning mechanism on VLAQ centers for RGB-LiDAR fusion. No step defines a quantity in terms of the target retrieval metric or renames a fitted parameter as a prediction. No self-citation is used to justify uniqueness or load-bearing assumptions. The reported gains (e.g., 61.1 R@1) are presented as outcomes of experiments on KITTI360-AG and nuScenes-AG rather than algebraic identities or self-referential fits. The chain is therefore self-contained against external benchmarks and pre-trained components.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The approach relies on external pre-trained foundation models and introduces a new fusion conditioning technique whose internal hyperparameters are not detailed.

pith-pipeline@v0.9.0 · 5569 in / 1212 out tokens · 52379 ms · 2026-05-12T02:52:37.601596+00:00 · methodology

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Reference graph

Works this paper leans on

72 extracted references · 72 canonical work pages

  1. [1]

    Ali-bey, B

    A. Ali-bey, B. Chaib-draa, and P. Giguère. GSV-Cities: Toward appropriate supervised visual place recognition.Neurocomputing, 513:194–203, 2022. doi: 10.1016/j.neucom.2022.09.127

    work page doi:10.1016/j.neucom.2022.09.127 2022

  2. [2]

    Localized gaussian splatting editing with contextual awareness

    A. Ali-bey, B. Chaib-draa, and P. Giguère. MixVPR: Feature mixing for visual place recognition. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2998–3007, 2023. doi: 10.1109/W ACV56688.2023.00301

    work page doi:10.1109/w 2023

  3. [3]

    In: 2024 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR)

    A. Ali-bey, B. Chaib-draa, and P. Giguère. BoQ: A place is worth a bag of learnable queries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17794–17803, 2024. doi: 10.1109/CVPR52733.2024.01685

    work page doi:10.1109/cvpr52733.2024.01685 2024

  4. [4]

    Arandjelovi´c, P

    R. Arandjelovi´c, P. Gronat, A. Torii, T. Pajdla, and J. Sivic. NetVLAD: CNN architecture for weakly supervised place recognition. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5297–5307, 2016. doi: 10.1109/CVPR.2016.572

    work page doi:10.1109/cvpr.2016.572 2016

  5. [5]

    nuScenes: A multimodal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom. nuscenes: A multimodal dataset for autonomous driving. In2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11618–11628, 2020. doi: 10.1109/CVPR42600.2020.01164

    work page doi:10.1109/cvpr42600.2020.01164 2020

  6. [6]

    Deuser, K

    F. Deuser, K. Habel, and N. Oswald. Sample4Geo: Hard negative sampling for cross-view geo-localisation. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 16847–16856, 2023

    work page 2023

  7. [7]

    In: IEEE International Conference on Robotics and Automation, ICRA 2024, Yokohama, Japan, May 13-17, 2024

    A. García-Hernández, R. Giubilato, K. H. Strobl, J. Civera, and R. Triebel. Unifying local and global multimodal features for place recognition in aliased and low-texture environments. InProceedings of the IEEE International Conference on Robotics and Automation, pages 3991–3998, 2024. doi: 10.1109/ICRA57147.2024.10611563

    work page doi:10.1109/icra57147.2024.10611563 2024

  8. [8]

    S. Garg, T. Fischer, and M. Milford. Where is your place, visual place recognition? In30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021

    work page 2021

  9. [9]

    S. Hu, M. Feng, R. M. H. Nguyen, and G. H. Lee. CVM-Net: Cross-view matching network for image-based ground-to-aerial geo-localization. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7258–7267, 2018

    work page 2018

  10. [10]

    Izquierdo and J

    S. Izquierdo and J. Civera. Close, but not there: Boosting geographic distance sensitivity in visual place recognition. InComputer Vision – ECCV 2024, pages 240–257, 2024. doi: 10.1007/978-3-031-73464-9_15

    work page doi:10.1007/978-3-031-73464-9_15 2024

  11. [11]

    Izquierdo and J

    S. Izquierdo and J. Civera. Optimal transport aggregation for visual place recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17658–17668, 2024

    work page 2024

  12. [12]

    M. Jung, L. F. T. Fu, M. Fallon, and A. Kim. ImLPR: Image-based LiDAR place recognition using vision foundation models. InProceedings of The 9th Conference on Robot Learning, volume 305 ofProceedings of Machine Learning Research, pages 3318–3340, 2025

    work page 2025

  13. [13]

    Jégou, M

    H. Jégou, M. Douze, C. Schmid, and P. Pérez. Aggregating local descriptors into a compact image representation. In2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3304–3311, 2010. doi: 10.1109/CVPR.2010.5540039

    work page doi:10.1109/cvpr.2010.5540039 2010

  14. [14]

    Keetha, A

    N. Keetha, A. Mishra, J. Karhade, K. M. Jatavallabhula, S. Scherer, M. Krishna, and S. Garg. AnyLoc: Towards universal visual place recognition.IEEE Robotics and Automation Letters, 9 (2):1286–1293, 2023. doi: 10.1109/LRA.2023.3343602

    work page doi:10.1109/lra.2023.3343602 2023

  15. [15]

    Going Beyond Accuracy: Interpretability Metrics for CNN Representations of Physiological Signals , shorttitle =

    J. Komorowski. Improving point cloud based place recognition with ranking-based loss and large batch training. InProceedings of the 26th International Conference on Pattern Recognition, pages 3699–3705, 2022. doi: 10.1109/ICPR56361.2022.9956458

    work page doi:10.1109/icpr56361.2022.9956458 2022

  16. [16]

    Komorowski, M

    J. Komorowski, M. Wysoczanska, and T. Trzcinski. MinkLoc++: Lidar and monocular image fusion for place recognition. arXiv preprint arXiv:2104.05327, 2021. URL https://arxiv. org/abs/2104.05327. 10

    work page arXiv 2021

  17. [17]

    H. Lai, P. Yin, and S. Scherer. AdaFusion: Visual-lidar fusion with adaptive weights for place recognition. arXiv preprint arXiv:2111.11739, 2021. URL https://arxiv.org/abs/2111. 11739

    work page arXiv 2021

  18. [18]

    G. Li, M. Qian, and G.-S. Xia. Unleashing unlabeled data: A paradigm for cross-view geo- localization. In2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16719–16729, 2024. doi: 10.1109/CVPR52733.2024.01582

    work page doi:10.1109/cvpr52733.2024.01582 2024

  19. [19]

    Y .-J. Li, M. Gladkova, Y . Xia, R. Wang, and D. Cremers. VXP: V oxel-cross-pixel large- scale camera-lidar place recognition. In2025 International Conference on 3D Vision, pages 1233–1242, 2025. doi: 10.1109/3DV66043.2025.00117

    work page doi:10.1109/3dv66043.2025.00117 2025

  20. [20]

    Li and T

    Z. Li and T. Shang. A2GC: Asymmetric aggregation with geometric constraints for locally aggregated descriptors. arXiv preprint arXiv:2511.14109, 2025. URL https://arxiv.org/ abs/2511.14109

    work page arXiv 2025

  21. [21]

    Y . Liao, J. Xie, and A. Geiger. Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2023. doi: 10.1109/TPAMI.2022.3179507

    work page doi:10.1109/tpami.2022.3179507 2023

  22. [22]

    Lindenberger, P.-E

    P. Lindenberger, P.-E. Sarlin, J. Hosang, M. Balice, M. Pollefeys, S. Lynen, and E. Trulls. Scaling image geo-localization to continent level. InThe Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), 2025

    work page 2025

  23. [23]

    Liu and H

    L. Liu and H. Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5624–5633, 2019

    work page 2019

  24. [24]

    Lowry, N

    S. Lowry, N. Sünderhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, and M. J. Milford. Visual place recognition: A survey.IEEE Transactions on Robotics, 32(1):1–19, 2016. doi: 10.1109/TRO.2015.2496823

    work page doi:10.1109/tro.2015.2496823 2016

  25. [25]

    F. Lu, X. Lan, L. Zhang, D. Jiang, Y . Wang, and C. Yuan. CricaVPR: Cross-image correlation- aware representation learning for visual place recognition. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16772–16782, 2024

    work page 2024

  26. [26]

    F. Lu, L. Zhang, X. Lan, S. Dong, Y . Wang, and C. Yuan. Towards seamless adaptation of pre-trained models for visual place recognition. InInternational Conference on Learning Representations, 2024

    work page 2024

  27. [27]

    F. Lu, T. Jin, X. Lan, L. Zhang, Y . Liu, Y . Wang, and C. Yuan. SelaVPR++: Towards seamless adaptation of foundation models for efficient place recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. doi: 10.1109/TPAMI.2025.3629287

    work page doi:10.1109/tpami.2025.3629287 2025

  28. [28]

    S. Lu, X. Xu, H. Yin, Z. Chen, R. Xiong, and Y . Wang. One ring to rule them all: Radon sinogram for place recognition, orientation and translation estimation. In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2778–2785, 2022. doi: 10.1109/IROS47612.2022.9981308

    work page doi:10.1109/iros47612.2022.9981308 2022

  29. [29]

    Y . Lu, F. Yang, F. Chen, and D. Xie. PIC-Net: Point cloud and image collaboration network for large-scale place recognition. arXiv preprint arXiv:2008.00658, 2020. URL https: //arxiv.org/abs/2008.00658

    work page arXiv 2008

  30. [30]

    L. Luo, S. Zheng, Y . Li, Y . Fan, B. Yu, S.-Y . Cao, J. Li, and H.-L. Shen. BEVPlace: Learning lidar-based place recognition using bird’s eye view images. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 8700–8709, 2023

    work page 2023

  31. [31]

    Maggio, H

    D. Maggio, H. Lim, and L. Carlone. VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold. InThe Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), 2025

    work page 2025

  32. [32]

    Melekhin, D

    A. Melekhin, D. A. Yudin, I. Petryashin, and V . D. Bezuglyj. MSSPlace: Multi-sensor place recognition with visual and text semantics.IEEE Access, 13:177098–177110, 2025. doi: 10.1109/ACCESS.2025.3618728. 11

    work page doi:10.1109/access.2025.3618728 2025

  33. [33]

    L. Mi, C. Xu, J. Castillo-Navarro, S. Montariol, W. Yang, A. Bosselut, and D. Tuia. Congeo: Robust cross-view geo-localization across ground view variations. InComputer Vision – ECCV 2024, pages 214–230, 2025

    work page 2024

  34. [34]

    Milford and T

    M. Milford and T. Fischer. Going places: Place recognition in artificial and natural systems. Annual Review of Control, Robotics, and Autonomous Systems, 9, 2025

    work page 2025

  35. [35]

    Y . Ming, X. Yang, G. Zhang, and A. Calway. Cgis-net: Aggregating colour, geometry and implicit semantic features for indoor place recognition. In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6991–6997, 2022. doi: 10.1109/ IROS47612.2022.9981113

    work page arXiv 2022

  36. [36]

    Y . Ming, J. Ma, X. Yang, W. Dai, Y . Peng, and W. Kong. Aegis-net: Attention-guided multi-level feature aggregation for indoor place recognition. InICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4030–4034, 2024. doi: 10.1109/ICASSP48485.2024.10447578

    work page doi:10.1109/icassp48485.2024.10447578 2024

  37. [37]

    D. Olid, J. M. Fácil, and J. Civera. Single-view place recognition under seasonal changes. arXiv preprint arXiv:1808.06516, 2018. URLhttps://arxiv.org/abs/1808.06516

    work page arXiv 2018

  38. [38]

    Oquab, T

    M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y . Huang, S.-W. Li, I. Misra, M. Rabbat, V . Sharma, G. Synnaeve, H. Xu, H. Jégou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski. DINOv2: Learning robust visual features without super...

    work page 2024

  39. [39]

    Z. Qi, J. Xu, L. Cheng, S. Wen, Y . Ma, and G. Xiong. UniMPR: A unified framework for multimodal place recognition with heterogeneous sensor configurations. arXiv preprint arXiv:2512.18279, 2025. URLhttps://arxiv.org/abs/2512.18279

    work page arXiv 2025

  40. [40]

    Radenovi´c, G

    F. Radenovi´c, G. Tolias, and O. Chum. Fine-tuning cnn image retrieval with no human annotation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(7):1655–1668,

    work page

  41. [41]

    doi: 10.1109/TPAMI.2018.2846566

    work page doi:10.1109/tpami.2018.2846566 2018

  42. [42]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever. Learning transferable visual models from natural language supervision. InProceedings of the 38th International Conference on Machine Learning, volume 139 ofProceedings of Machine Learning Research, pages 8748–8763, 2021

    work page 2021

  43. [43]

    Samano, M

    N. Samano, M. Zhou, and A. Calway. You are here: Geolocation by embedding maps and images. InComputer Vision – ECCV 2020, pages 502–518, 2020

    work page 2020

  44. [44]

    Schubert, P

    S. Schubert, P. Neubert, S. Garg, M. Milford, and T. Fischer. Visual place recognition: A tutorial [tutorial].IEEE Robotics & Automation Magazine, 31(3):139–153, 2023

    work page 2023

  45. [45]

    Serio, G

    P. Serio, G. Pisaneschi, A. D. Ryals, V . Infantino, L. Gentilini, V . Donzella, and L. Pollini. Polar perspectives: Evaluating 2-d lidar projections for robust place recognition with visual foundation models. arXiv preprint arXiv:2512.02897, 2025. URL https://arxiv.org/abs/ 2512.02897

    work page arXiv 2025

  46. [46]

    Y . Shi, L. Liu, X. Yu, and H. Li. Spatial-aware feature aggregation for image based cross-view geo-localization. InAdvances in Neural Information Processing Systems, volume 32, 2019

    work page 2019

  47. [47]

    Y . Shi, X. Yu, L. Liu, T. Zhang, and H. Li. Optimal feature transport for cross-view image geo-localization. InProceedings of the AAAI Conference on Artificial Intelligence, pages 11990–11997, 2020. doi: 10.1609/aaai.v34i07.6875

    work page doi:10.1609/aaai.v34i07.6875 2020

  48. [48]

    Shubodh, M

    S. Shubodh, M. Omama, H. Zaidi, U. S. Parihar, and M. Krishna. LIP-Loc: Lidar image pretraining for cross-modal localization. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, pages 948–957, 2024

    work page 2024

  49. [49]

    Shugaev, I

    M. Shugaev, I. Semenov, K. Ashley, M. Klaczynski, N. Cuntoor, M. W. Lee, and N. Jacobs. ArcGeo: Localizing limited field-of-view images using cross-view matching. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 209–218, 2024. 12

    work page 2024

  50. [50]

    Sivic and A

    J. Sivic and A. Zisserman. Efficient visual search of videos cast as text retrieval.IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4):591–606, 2009. doi: 10.1109/ TPAMI.2008.111

    work page 2009

  51. [51]

    Suomela, J

    L. Suomela, J. Kalliola, H. Edelman, and J.-K. Kämäräinen. Placenav: Topological navigation through place recognition. InIEEE International Conference on Robotics and Automation (ICRA), 2024. URLhttps://arxiv.org/abs/2309.17260

    work page arXiv 2024

  52. [52]

    Torii, J

    A. Torii, J. Sivic, T. Pajdla, and M. Okutomi. Visual place recognition with repetitive structures. In2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 883–890, 2013. doi: 10.1109/CVPR.2013.119

    work page doi:10.1109/cvpr.2013.119 2013

  53. [53]

    Torii, R

    A. Torii, R. Arandjelovi´c, J. Sivic, M. Okutomi, and T. Pajdla. 24/7 place recognition by view synthesis.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2):257–271,

    work page

  54. [54]

    doi: 10.1109/TPAMI.2017.2667665

    work page doi:10.1109/tpami.2017.2667665 2017

  55. [55]

    M. A. Uy and G. H. Lee. Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition. In2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4470–4479, 2018. doi: 10.1109/CVPR.2018.00470

    work page doi:10.1109/cvpr.2018.00470 2018

  56. [56]

    S. Wang, R. She, Q. Kang, S. Li, D. Li, T. Geng, S. Yu, and W. P. Tay. Multi-modal aerial-ground cross-view place recognition with neural odes. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11717–11728, 2025

    work page 2025

  57. [57]

    T. Wang, Z. Zheng, C. Yan, J. Zhang, Y . Sun, B. Zheng, and Y . Yang. Each part matters: Local patterns facilitate cross-view geo-localization.IEEE Transactions on Circuits and Systems for Video Technology, 32(2):867–879, 2022. doi: 10.1109/TCSVT.2021.3061265

    work page doi:10.1109/tcsvt.2021.3061265 2022

  58. [58]

    nuScenes: A multimodal dataset for autonomous driving,

    F. Warburg, S. Hauberg, M. López-Antequera, P. Gargallo, Y . Kuang, and J. Civera. Mapillary street-level sequences: A dataset for lifelong place recognition. In2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2623–2632, 2020. doi: 10.1109/ CVPR42600.2020.00270

    work page arXiv 2020

  59. [59]

    Workman, R

    S. Workman, R. Souvenir, and N. Jacobs. Wide-area image geolocalization with aerial reference imagery. InProceedings of the IEEE International Conference on Computer Vision, pages 3961–3969, 2015

    work page 2015

  60. [60]

    Y . Xia, Z. Li, Y .-J. Li, L. Shi, H. Cao, J. F. Henriques, and D. Cremers. UniLoc: Towards universal place recognition using any single modality. arXiv preprint arXiv:2412.12079, 2024. URLhttps://arxiv.org/abs/2412.12079

    work page arXiv 2024

  61. [61]

    W. Xie, L. Luo, N. Ye, Y . Ren, S. Du, M. Wang, J. Xu, R. Ai, W. Gu, and X. Chen. ModaLink: Unifying modalities for efficient image-to-pointcloud place recognition. In2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3326–3333, 2024. doi: 10.1109/IROS58592.2024.10801556

    work page doi:10.1109/iros58592.2024.10801556 2024

  62. [62]

    X. Xu, S. Lu, J. Wu, H. Lu, Q. Zhu, Y . Liao, R. Xiong, and Y . Wang. Ring++: Roto-translation invariant gram for global localization on a sparse scan map.IEEE Transactions on Robotics, 39 (6):4616–4635, 2023. doi: 10.1109/TRO.2023.3303035

    work page doi:10.1109/tro.2023.3303035 2023

  63. [63]

    H. Yang, X. Lu, and Y . Zhu. Cross-view geo-localization with layer-to-layer transformer. In Advances in Neural Information Processing Systems, volume 34, pages 29009–29020, 2021

    work page 2021

  64. [64]

    J. Ye, Z. Lv, W. Li, J. Yu, H. Yang, H. Zhong, and C. He. Cross-view image geo-localization with panorama-bev co-retrieval network. InComputer Vision – ECCV 2024, pages 74–90, 2025

    work page 2024

  65. [65]

    Zaffar, L

    M. Zaffar, L. Nan, and J. F. P. Kooij. Copr: Toward accurate visual localization with continuous place-descriptor regression.IEEE Transactions on Robotics, 39(4):2825–2841, 2023

    work page 2023

  66. [66]

    Zhang and Y

    Q. Zhang and Y . Zhu. Aligning geometric spatial layout in cross-view geo-localization via feature recombination.Proceedings of the AAAI Conference on Artificial Intelligence, 38(7): 7251–7259, 2024. doi: 10.1609/aaai.v38i7.28554. 13

    work page doi:10.1609/aaai.v38i7.28554 2024

  67. [67]

    Zhang, X

    X. Zhang, X. Li, W. Sultani, Y . Zhou, and S. Wshah. Cross-view geo-localization via learning disentangled geometric layout correspondence.Proceedings of the AAAI Conference on Artificial Intelligence, 37(3):3480–3488, 2023. doi: 10.1609/aaai.v37i3.25457

    work page doi:10.1609/aaai.v37i3.25457 2023

  68. [68]

    Zhang, X

    Y . Zhang, X. Wu, Y . Yang, X. Fan, H. Li, Y . Zhang, Z. Huang, N. Wang, and H. Zhao. Utonia: Toward one encoder for all point clouds. arXiv preprint arXiv:2603.03283, 2026. URL https://arxiv.org/abs/2603.03283

    work page arXiv 2026

  69. [69]

    Z. Zhou, J. Xu, G. Xiong, and J. Ma. LCPR: A multi-scale attention-based lidar-camera fusion network for place recognition.IEEE Robotics and Automation Letters, 9(2):1342–1349, 2024. doi: 10.1109/LRA.2023.3346753

    work page doi:10.1109/lra.2023.3346753 2024

  70. [70]

    H. Zhu, Z. Zhan, Y . Ming, L. Li, D. Hou, J. Civera, and W. Kong. DC-VLAQ: Query-residual aggregation for robust visual place recognition. arXiv preprint arXiv:2601.12729, 2026. URL https://arxiv.org/abs/2601.12729

    work page arXiv 2026

  71. [71]

    S. Zhu, T. Yang, and C. Chen. VIGOR: Cross-view image geo-localization beyond one-to- one retrieval. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3640–3649, 2021

    work page 2021

  72. [72]

    S. Zhu, M. Shah, and C. Chen. TransGeo: Transformer is all you need for cross-view image geo-localization. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1162–1171, 2022. 14 A Attention Visualization Figure 4: Attention Visualization Fig. 4 provides qualitative attention visualizations for the ground RGB image, ...

    work page 2022