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Investigating the Role of Image Retrieval for Visual Localization

An Exhaustive Benchmark

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International Journal of Computer Vision Aims and scope Submit manuscript

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 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 https://github.com/naver/kapture-localization.

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Notes

  1. \({\mathbf {q}}_q = \sum _i w_i {\mathbf {q}}_i\) is re-normalized to be a unit quaternion.

  2. Note that compared to the query pose, the 3D points are very seldom co-linear with the reference poses and can thus be accurately triangulated.

  3. Note that only datasets with publicly available ground truth are used to generate these results. For Aachen Day-Night and InLoc, no GT poses were available (see Sect. 4.1).

  4. Code available at http://www.ok.ctrl.titech.ac.jp/~torii/project/247/.

  5. Matlab code and pretrained models are available at https://github.com/Relja/netvlad. We used the VGG-16-based NetVLAD model trained on Pitts30k (Arandjelović et al. 2016).

  6. Pytorch implementation and models are available at https://europe.naverlabs.com/Research/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval/.

  7. We used the TensorFlow code publicly available at https://github.com/tensorflow/models/tree/master/research/delf/delf/python/delg.

  8. Note that this is different from the model used in our 3DV paper (Pion et al. 2020), where we used a model with ResNet50 backbone trained on GLD v1.

  9. For InLoc, the viewpoint difference between the reference images is too large to allow robust feature matching and point triangulation. Using the available depth maps to obtain the 3D points for all features for local SFM is identical to the way we perform global SFM on InLoc. That is why for InLoc, we do not show results for local SFM.

  10. For the other datasets, GT camera poses are not available for the test images, which makes it hard to generate retrieval GT.

  11. https://www.pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/.

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Acknowledgements

This work received funding through the EU Horizon 2020 research and innovation programme under Grant agreement No. 857306 (RICAIP) and the European Regional Development Fund under IMPACT No. CZ.02.1.01/0.0/0.0/15_003/0000468.

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Authors and Affiliations

  1. NAVER LABS Europe, Meylan, France

    Martin Humenberger, Yohann Cabon, Noé Pion, Philippe Weinzaepfel, Nicolas Guérin & Gabriela Csurka

  2. NAVER LABS, Seongnam-si, Republic of Korea

    Donghwan Lee

  3. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

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  1. Martin Humenberger
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Communicated by Jun Sato.

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Noé Pion work was done during an appointment at NAVER LABS Europe, Meylan, France.

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Humenberger, M., Cabon, Y., Pion, N. et al. Investigating the Role of Image Retrieval for Visual Localization. Int J Comput Vis 130, 1811–1836 (2022). https://doi.org/10.1007/s11263-022-01615-7

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