NPR: Nocturnal Place Recognition in Streets

• URL: http://arxiv.org/abs/2304.00276v2
• Date: Mon, 17 Apr 2023 16:28:47 GMT
• Title: NPR: Nocturnal Place Recognition in Streets
• Authors: Bingxi Liu, Yujie Fu, Feng Lu, Jinqiang Cui, Yihong Wu, Hong Zhang
• Abstract summary: We propose a novel pipeline that divides Visual Place Recognition (VPR) and conquers Nocturnal Place Recognition (NPR) Specifically, we first established a street-level day-night dataset, NightStreet, and used it to train an unpaired image-to-image translation model. Then we used this model to process existing large-scale VPR datasets to generate the VPR-Night datasets and demonstrated how to combine them with two popular VPR pipelines.
• Score: 15.778129994700496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Place Recognition (VPR) is the task of retrieving database images similar to a query photo by comparing it to a large database of known images. In real-world applications, extreme illumination changes caused by query images taken at night pose a significant obstacle that VPR needs to overcome. However, a training set with day-night correspondence for city-scale, street-level VPR does not exist. To address this challenge, we propose a novel pipeline that divides VPR and conquers Nocturnal Place Recognition (NPR). Specifically, we first established a street-level day-night dataset, NightStreet, and used it to train an unpaired image-to-image translation model. Then we used this model to process existing large-scale VPR datasets to generate the VPR-Night datasets and demonstrated how to combine them with two popular VPR pipelines. Finally, we proposed a divide-and-conquer VPR framework and provided explanations at the theoretical, experimental, and application levels. Under our framework, previous methods can significantly improve performance on two public datasets, including the top-ranked method.

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