Beyond Controlled Environments: 3D Camera Re-localization in Changing Indoor Scenes
Paper i proceeding, 2020

Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular indoor benchmarks, which focus on static scenes, and indoor environments that are of interest for many real-world applications. In this paper, we adapt 3RScan – a recently introduced indoor RGB-D dataset designed for object instance re-localization – to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes. We propose new metrics for evaluating camera re-localization and explore how state-of-the-art camera re-localizers perform according to these metrics. We also examine in detail how different types of scene change affect the performance of different methods, based on novel ways of detecting such changes in a given RGB-D frame. Our results clearly show that long-term indoor re-localization is an unsolved problem. Our benchmark and tools are publicly available at https://www.waldjohannau.github.io/RIO10.

Författare

Johanna Wald

Technische Universität München

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Ceske Vysoke Uceni Technicke v Praze

Stuart Golodetz

FiveAI

Tommaso Cavallari

FiveAI

Federico Tombari

Technische Universität München

Google Inc.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12352 LNCS 467-487

16th European Conference on Computer Vision, ECCV 2020
Glasgow, United Kingdom,

Ämneskategorier

Annan data- och informationsvetenskap

Robotteknik och automation

Datorseende och robotik (autonoma system)

DOI

10.1007/978-3-030-58571-6_28

Mer information

Senast uppdaterat

2020-12-21