Beyond Controlled Environments: 3D Camera Re-localization in Changing Indoor Scenes
Paper in 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


Johanna Wald

Technical University of Munich

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Czech Technical University in Prague

Stuart Golodetz


Tommaso Cavallari


Federico Tombari

Technical University of Munich

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
9783030585709 (ISBN)

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

Subject Categories

Other Computer and Information Science


Computer Vision and Robotics (Autonomous Systems)



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