Inside Out Visual Place Recognition
This addresses the challenge of indoor localization for applications like robotics or augmented reality, but it is incremental as it adapts existing VPR methods to a new scenario.
The paper tackles the problem of localizing indoor images based on outdoor scenes visible through windows, introducing the Inside Out Visual Place Recognition (IOVPR) task and a new large-scale dataset Amsterdam-XXXL with 6.4 million panoramic street-view images and 1000 indoor queries, and proposes a data augmentation scheme that shows benefits on a smaller scale while highlighting the dataset's difficulty for existing methods.
Visual Place Recognition (VPR) is generally concerned with localizing outdoor images. However, localizing indoor scenes that contain part of an outdoor scene can be of large value for a wide range of applications. In this paper, we introduce Inside Out Visual Place Recognition (IOVPR), a task aiming to localize images based on outdoor scenes visible through windows. For this task we present the new large-scale dataset Amsterdam-XXXL, with images taken in Amsterdam, that consists of 6.4 million panoramic street-view images and 1000 user-generated indoor queries. Additionally, we introduce a new training protocol Inside Out Data Augmentation to adapt Visual Place Recognition methods for localizing indoor images, demonstrating the potential of Inside Out Visual Place Recognition. We empirically show the benefits of our proposed data augmentation scheme on a smaller scale, whilst demonstrating the difficulty of this large-scale dataset for existing methods. With this new task we aim to encourage development of methods for IOVPR. The dataset and code are available for research purposes at https://github.com/saibr/IOVPR