In this paper, we propose a new panoramic image-based rendering method for novel view generation. Therefore these methods do not support free-viewpoint navigation. Although there are methods that can synthesize novel views based on captured panoramas, the generated novel views all lie on the lines connecting existing views. However, due to bandwidth or storage limitations, existing systems only provide sparsely captured panoramas and have limited interaction modes. Thus, they are widely used in virtual reality systems and street view services. Under our framework, previous methods can significantly improve performance on two public datasets, including the top-ranked method.īy providing a 360° field of view, spherical panoramas can convey vivid visual impressions.
Finally, we proposed a divide-and-conquer VPR framework and provided explanations at the theoretical, experimental, and application levels. 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. Specifically, we first established a street-level day-night dataset, NightStreet, and used it to train an unpaired image-to-image translation model. To address this challenge, we propose a novel pipeline that divides VPR and conquers Nocturnal Place Recognition (NPR). However, a training set with day-night correspondence for city-scale, street-level VPR does not exist. In real-world applications, extreme illumination changes caused by query images taken at night pose a significant obstacle that VPR needs to overcome. 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.
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