MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911451479801856 |
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| author | Ou, Yiwei Ren, Xiaobin Sun, Ronggui Gao, Guansong Zhao, Kaiqi Manfredini, Manfredo |
| author_facet | Ou, Yiwei Ren, Xiaobin Sun, Ronggui Gao, Guansong Zhao, Kaiqi Manfredini, Manfredo |
| contents | Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and underrepresent dense pedestrian street scenes, particularly in non-Western urban contexts. We introduce MMS-VPR, a large-scale multimodal dataset for street-level place recognition in pedestrian-only environments. MMS-VPR comprises 110,529 images and 2,527 video clips across 208 locations in a ~70,800 $m^2$ open-air commercial district in Chengdu, China. Field data were collected in 2024, while social media data span seven years (2019-2025), providing both fine-grained temporal granularity and long-term temporal coverage. Each location features comprehensive day-night coverage, multiple viewing angles, and multimodal annotations including GPS coordinates, timestamps, and semantic textual metadata. We further release MMS-VPRlib, a unified benchmarking platform that consolidates commonly used VPR datasets and state-of-the-art methods under a standardized, reproducible pipeline. MMS-VPRlib provides modular components for data pre-processing, multimodal modeling (CNN/RNN/Transformer), signal enhancement, alignment, fusion, and performance evaluation. This platform moves beyond traditional image-only paradigms, enabling systematic exploitation of complementary visual, video, and textual modalities. The dataset is available at https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR and the benchmark at https://github.com/yiasun/MMS-VPRlib. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark Ou, Yiwei Ren, Xiaobin Sun, Ronggui Gao, Guansong Zhao, Kaiqi Manfredini, Manfredo Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and underrepresent dense pedestrian street scenes, particularly in non-Western urban contexts. We introduce MMS-VPR, a large-scale multimodal dataset for street-level place recognition in pedestrian-only environments. MMS-VPR comprises 110,529 images and 2,527 video clips across 208 locations in a ~70,800 $m^2$ open-air commercial district in Chengdu, China. Field data were collected in 2024, while social media data span seven years (2019-2025), providing both fine-grained temporal granularity and long-term temporal coverage. Each location features comprehensive day-night coverage, multiple viewing angles, and multimodal annotations including GPS coordinates, timestamps, and semantic textual metadata. We further release MMS-VPRlib, a unified benchmarking platform that consolidates commonly used VPR datasets and state-of-the-art methods under a standardized, reproducible pipeline. MMS-VPRlib provides modular components for data pre-processing, multimodal modeling (CNN/RNN/Transformer), signal enhancement, alignment, fusion, and performance evaluation. This platform moves beyond traditional image-only paradigms, enabling systematic exploitation of complementary visual, video, and textual modalities. The dataset is available at https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR and the benchmark at https://github.com/yiasun/MMS-VPRlib. |
| title | MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.12254 |