Back to Search Start Over

Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark

Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark

Authors :
Humenberger, Martin
Cabon, Yohann
Pion, Noé
Weinzaepfel, Philippe
Lee, Donghwan
Guérin, Nicolas
Sattler, Torsten
Csurka, Gabriela
Humenberger, Martin
Cabon, Yohann
Pion, Noé
Weinzaepfel, Philippe
Lee, Donghwan
Guérin, Nicolas
Sattler, Torsten
Csurka, Gabriela
Publication Year :
2022

Abstract

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 sgnificant 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 https://github.com/naver/kapture-localization.<br />Comment: International Journal of Computer Vision (2022). arXiv admin note: text overlap with arXiv:2011.11946

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1333775364
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1007.s11263-022-01615-7