24/7 Place Recognition by View Synthesis (Open Access)
Tokyo Tech Tokyo Japan
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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 daynight, change of seasons, aging, or structural modifications over time such as buildings 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.