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
翻译：视觉地点识别(VPR)通常与室外图像本地化有关,然而,包含户外场景一部分内容的室内场景本地化对于广泛的应用可能具有巨大价值。我们在本文件中引入了“内视场识别(IOVPR)”这一任务,目的是在窗口可见的户外场景上将图像本地化。我们为这项任务介绍了新的大型数据集阿姆斯特丹-XXXL(在阿姆斯特丹拍摄的图像),其中包括640万全景街景图像和1000个用户生成的室内查询。此外,我们引入了新的培训协议,以调整室内图像本地化的视觉地点识别方法,展示了内视场识别的潜力。我们从经验上展示了我们所提议的数据增强计划在较小规模上的好处,同时展示了这一大规模数据集对现有方法的难度。我们的新任务旨在鼓励为IOVPR开发方法。数据集和代码可在https://github.com/saibr/IOVPR进行研究。
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