Inside Out Visual Place Recognition

TL;DR

This paper introduces Inside Out Visual Place Recognition (IOVPR), a new task for localizing indoor images with outdoor scenes visible through windows, supported by a large-scale dataset and a novel data augmentation protocol.

Contribution

The paper presents a new large-scale dataset Amsterdam-XXXL and a training protocol Inside Out Data Augmentation for IOVPR, advancing indoor-outdoor scene localization research.

Findings

Data augmentation improves indoor scene localization.

Existing methods struggle with large-scale IOVPR dataset.

The dataset and code are publicly available.

Abstract

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