Investigating the Role of Image Retrieval for Visual Localization

Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimat...

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Bibliographic Details
Published in:International journal of computer vision 2022-07, Vol.130 (7), p.1811-1836
Main Authors: Humenberger, Martin, Cabon, Yohann, Pion, Noé, Weinzaepfel, Philippe, Lee, Donghwan, Guérin, Nicolas, Sattler, Torsten
Format: Article
Language:English
Subjects:
Algorithms
Augmented Reality
Benchmarks
Image management
Image retrieval
Investigations
Localization
Pose estimation
Upper bounds
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Summary:Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of "ground truth" for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-022-01615-7