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AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition

Summary

Visual Place Recognition (VPR) is often hurt by domain shifts such as lighting, weather, seasons, and dynamic occlusions, partly because training data lack appearance diversity for the same place. AdaptVPR is a route-aware generative augmentation framework that creates same-place hard positives using a vision-language model for scene understanding and a rule-based scheduler for route selection. Global appearance, local occlusion, and dual routes are verified through geometric consistency and appearance diversity, producing AdaptCities, a dataset of 160K validated synthetic hard positives. Experiments across VPR baselines and vision foundation backbones show consistent R@1 improvements, up to 9.2% under challenging shifts.

SourcearXiv Computer VisionAuthor: Shunpeng Chen, Jingyi Zhang, Changwei Wang, Shengpeng Xu, Yukun Song, Xingtian Pei, Jinzhou Lin, Li Guo, Shibiao Xu
AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition
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[Submitted on 3 Sep 2026]

Title:AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition

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Abstract:Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby place, yet its robustness is often degraded by domain shifts arising from illumination, weather, seasonal changes, and dynamic occlusions. One contributing factor is the limited appearance diversity of the same place in existing training data. To address this issue, we propose AdaptVPR, a route-aware generative augmentation framework that constructs same-place hard positives for robust VPR training. AdaptVPR first uses a vision language model to parse scene attributes and estimate editing feasibility, while a rule-based scheduler determines the generation route according to editability scores and risk constraints. The generation process is decomposed into three complementary routes: the Global Appearance Route introduces global scene changes in weather, illumination, and time of day; the Local Occlusion Route inserts plausible dynamic occluders; and the Dual Route combines both types of perturbations to produce more challenging appearance shifts. Each generated candidate is evaluated using a VPR-oriented verification scheme based on geometric consistency and appearance diversity, reducing the risk of structural drift while ensuring sufficient appearance variation. Global candidates are generated once and rejected if verification fails, while Local Occlusion and Dual candidates use verification feedback for limited prompt refinement and regeneration. Using this framework, we construct AdaptCities, containing 160K verified synthetic same-place hard positives. Experiments across multiple VPR baselines and vision foundation backbones show consistent gains on standard benchmarks and substantial improvements under challenging domain shifts, with R@1 gains of up to 9.2%. The source code and data resources are publicly available at this https URL.

Comments: 18 pages, 9 figures, 9 tables

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2609.04369 [cs.CV]

(or arXiv:2609.04369v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.04369

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shunpeng Chen [view email] [v1] Thu, 3 Sep 2026 18:34:17 UTC (26,699 KB)

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Key points and analysis

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Key points

  • AdaptVPR addresses limited same-place appearance diversity, a key cause of VPR robustness degradation.
  • Generation is decomposed into global appearance, local occlusion, and dual routes selected by a rule-based scheduler.
  • VPR-oriented verification uses geometric consistency and appearance diversity to reduce structural drift.
  • AdaptCities contains 160K verified synthetic hard positives and improves Recall@1 by up to 9.2% on hard domain shifts.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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