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.

翻译：摘要：视觉地点识别（VPR）是通过将查询照片与已知图像的大型数据库进行比对，检索出相似数据库图像的任务。在实际应用中，夜间拍摄的查询图像所导致的极端光照变化，成为VPR需克服的重大障碍。然而，针对城市场景下的街道级VPR，目前尚不存在具备昼夜对应关系的训练数据集。为应对这一挑战，我们提出了一种将VPR分而治之的新型流水线，即夜间地点识别（NPR）。具体而言，我们首先构建了街道级昼夜数据集NightStreet，并利用该数据集训练了非配对图像到图像的转换模型。随后，运用该模型处理现有的大规模VPR数据集，生成VPR-Night数据集，并展示了如何将其与两种主流VPR流水线进行结合。最后，我们提出了一种分治式VPR框架，并从理论、实验和应用层面给出了解释。在该框架下，包括排名最高的方法在内的已有方法，在两个公开数据集上的性能均能得到显著提升。