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NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer

Authors :
Liu, Bingxi
Wang, Yiqun
Tao, Huaqi
Huang, Tingjun
Tang, Fulin
Wu, Yihong
Cui, Jinqiang
Zhang, Hong
Liu, Bingxi
Wang, Yiqun
Tao, Huaqi
Huang, Tingjun
Tang, Fulin
Wu, Yihong
Cui, Jinqiang
Zhang, Hong
Publication Year :
2024

Abstract

Visual Place Recognition (VPR) is crucial in computer vision, aiming to retrieve database images similar to a query image from an extensive collection of known images. However, like many vision tasks, VPR always degrades at night due to the scarcity of nighttime images. Moreover, VPR needs to address the cross-domain problem of night-to-day rather than just the issue of a single nighttime domain. In response to these issues, we present NocPlace, which leverages generative and inherited knowledge transfer to embed resilience against dazzling lights and extreme darkness in the global descriptor. First, we establish a day-night urban scene dataset called NightCities, capturing diverse lighting variations and dark scenarios across 60 cities globally. Then, an image generation network is trained on this dataset and processes a large-scale VPR dataset, obtaining its nighttime version. Finally, VPR models are fine-tuned using descriptors inherited from themselves and night-style images, which builds explicit cross-domain contrastive relationships. Comprehensive experiments on various datasets demonstrate our contributions and the superiority of NocPlace. Without adding any real-time computing resources, NocPlace improves the performance of Eigenplaces by 7.6% on Tokyo 24/7 Night and 16.8% on SVOX Night.<br />Comment: 28 pages,9 figures

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1438529761
Document Type :
Electronic Resource