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Handcrafted Outlier Detection Revisited

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Book cover Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12364))

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Abstract

Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, have been proposed over the last decades. In recent years, deep learning-based approaches to outlier detection have become popular. Unfortunately, the corresponding works rarely compare with strong classical baselines. In this paper we revisit handcrafted approaches to outlier filtering. Based on best practices, we propose a hierarchical pipeline for effective outlier detection as well as integrate novel ideas which in sum lead to an efficient and competitive approach to outlier rejection. We show that our approach, although not relying on learning, is more than competitive to both recent learned works as well as handcrafted approaches, both in terms of efficiency and effectiveness. The code is available at https://github.com/cavalli1234/AdaLAM.

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Acknowledgements

This work was supported by a Google Focused Research Award, by the Swedish Foundation for Strategic Research (Semantic Mapping and Visual Navigation for Smart Robots), the Chalmers AI Research Centre (CHAIR) (VisLocLearn) and Innosuisse funding (Grant No. 34475.1 IP-ICT). Viktor Larsson was supported by an ETH Zurich Postdoctoral Fellowship.

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Authors and Affiliations

  1. Department of Computer Science, ETH Zurich, Zürich, Switzerland

    Luca Cavalli, Viktor Larsson, Martin Ralf Oswald & Marc Pollefeys

  2. Chalmers University of Technology, Gothenburg, Sweden

    Torsten Sattler

  3. Microsoft Mixed Reality & AI Zurich Lab, Zürich, Switzerland

    Marc Pollefeys

Authors
  1. Luca Cavalli
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  2. Viktor Larsson
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  3. Martin Ralf Oswald
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  4. Torsten Sattler
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  5. Marc Pollefeys
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Corresponding author

Correspondence to Viktor Larsson .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Cavalli, L., Larsson, V., Oswald, M.R., Sattler, T., Pollefeys, M. (2020). Handcrafted Outlier Detection Revisited. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12364. Springer, Cham. https://doi.org/10.1007/978-3-030-58529-7_45

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  • DOI: https://doi.org/10.1007/978-3-030-58529-7_45

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