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Correspondence Reweighted Translation Averaging

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13693))

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Abstract

Translation averaging methods use the consistency of input translation directions to solve for camera translations. However, translation directions obtained using epipolar geometry are error-prone. This paper argues that the improved accuracy of translation averaging should be leveraged to mitigate the errors in the input translation direction estimates. To this end, we introduce weights for individual correspondences which are iteratively refined to yield improved translation directions. In turn, these refined translation directions are averaged to obtain camera translations. This results in an alternating approach to translation averaging. The modularity of our framework allows us to use existing translation averaging methods and improve their results. The efficacy of the scheme is demonstrated by comparing performance with state-of-the-art methods on a number of real-world datasets. We also show that our approach yields reasonably good 3D reconstructions with straightforward triangulation, i.e. without any bundle adjustment iterations.

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Notes

  1. 1.

    For large datasets with number of cameras greater than 2000, \(N_{max}\) equals 30 and 5 for CReTA-RLUD and CReTA-BATA respectively.

  2. 2.

    https://ee.iisc.ac.in/cvlab/research/rotaveraging/.

  3. 3.

    https://bbzh.github.io/document/BATA.zip.

  4. 4.

    https://github.com/wilsonkl/SfM_Init.

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Acknowledgements

Lalit Manam is supported by a Prime Minister’s Research Fellowship, Government of India. This research was supported in part by a Core Research Grant from Science and Engineering Research Board, Department of Science and Technology, Government of India.

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Authors and Affiliations

  1. Indian Institute of Science, Bengaluru, 560012, India

    Lalit Manam & Venu Madhav Govindu

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  1. Lalit Manam
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  2. Venu Madhav Govindu
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Corresponding author

Correspondence to Venu Madhav Govindu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Manam, L., Govindu, V.M. (2022). Correspondence Reweighted Translation Averaging. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13693. Springer, Cham. https://doi.org/10.1007/978-3-031-19827-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-19827-4_4

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  • Online ISBN: 978-3-031-19827-4

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