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Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1815))

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Abstract

When faced with data corrupted by both noise and outliers robust estimation algorithms like RanSaC are used, especially in the field of Multi-View Stereo (MVS) or Structure-from-Motion (SfM). To find the best model fitting the data, from numerous minimal samples it evaluates models and rates them according to their number of inliers. The classification as inlier depends on a user-set threshold that should be tailored to the noise level of the data. This requires the knowledge of this level, which is rarely available. The few existing adaptive threshold algorithms solve this problem by estimating the value of the threshold while computing the best model. However, it is hard to obtain ground-truth for MVS and SfM tasks and usually test datasets are based on the output on some state of the art algorithm, which prevents the objective evaluation of new algorithms. We propose a new method to generate artificial datasets based on true data to get realistic and measurable results. We use this method to benchmark different automatic RanSaC algorithms and find out how they compare to each other and identify each algorithm’s strengths and weaknesses. This study reveals uses cases for each method and the possible tradeoffs between performance and execution time.

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Notes

  1. 1.

    Multi-H, kusvod2 and homogr can be found at http://cmp.felk.cvut.cz/data/geometry2view/.

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Authors and Affiliations

  1. Université Paris-Est, LIGM (UMR CNRS 8049), UGE, ENPC, 77455, Marne-la-Vallée, France

    Clément Riu, Vincent Nozick & Pascal Monasse

Authors
  1. Clément Riu
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  2. Vincent Nozick
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  3. Pascal Monasse
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Corresponding author

Correspondence to Clément Riu .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Augusto de Sousa

  2. University of Warwick, Coventry, UK

    Kurt Debattista

  3. Mines ParisTech, Paris, France

    Alexis Paljic

  4. Bentley University, Waltham, USA

    Mounia Ziat

  5. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  6. Monash University, Melbourne, VIC, Australia

    Helen Purchase

  7. Department of Mathematics, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  8. Computer Vision Center, University of Barcelona, Barcelona, Spain

    Petia Radeva

  9. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

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Riu, C., Nozick, V., Monasse, P. (2023). Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2022. Communications in Computer and Information Science, vol 1815. Springer, Cham. https://doi.org/10.1007/978-3-031-45725-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-45725-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45724-1

  • Online ISBN: 978-3-031-45725-8

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