We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings being built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.

中文翻译：

---

通过视图合成进行 24/7 地点识别


我们解决了场景外观发生重大变化的情况下的大规模视觉位置识别问题，例如，由于照明（白天/夜晚）、季节变化、老化或结构随时间的变化（例如建筑物被破坏）建造或摧毁。这种情况对当前大规模地点识别方法提出了重大挑战。这项工作有以下三个主要贡献。首先，我们证明，当查询图像和数据库图像从大致相同的视点描绘场景时，场景外观的较大变化的匹配变得更加容易。其次，基于这一观察，我们开发了一种新的地点识别方法，该方法将（i）新颖视图的有效合成与（ii）紧凑的可索引图像表示相结合。第三，我们引入了一个新的具有挑战性的数据集，其中包含东京的 1,125 张拍照手机查询图像，其中包含照明（白天、日落、夜晚）的主要变化以及场景的结构变化。我们证明，在这种具有挑战性的数据上，所提出的方法明显优于其他大规模地点识别技术。