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Geometric Estimation via Robust Subspace Recovery

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12367))

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Abstract

Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques. In this paper, we consider the problem from an optimization perspective, to exploit the intrinsic linear structure of point correspondences to assist estimation. We generalize the conventional method to a robust one and extend the previous analysis for linear structure to develop several new algorithms. The proposed solutions essentially address the estimation problem by solving a subspace recovery problem to identify the inliers. Experiments on real-world image datasets for both fundamental matrix and homography estimation demonstrate the superiority of our method over the state-of-the-art in terms of both robustness and accuracy.

J. Ma—This work was supported by the National Natural Science Foundation of China under Grant nos. 61773295 and 61971165.

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Notes

  1. 1.

    http://cmp.felk.cvut.cz/data/geometry2view/.

  2. 2.

    http://cmp.felk.cvut.cz/wbs/.

  3. 3.

    http://cmp.felk.cvut.cz/data/geometry2view/.

  4. 4.

    https://cs.adelaide.edu.au/~hwong/doku.php?id=data.

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Author information

Authors and Affiliations

  1. Wuhan University, Wuhan, 430072, China

    Aoxiang Fan, Xingyu Jiang, Yang Wang & Jiayi Ma

  2. Harbin Institute of Technology, Harbin, 150001, China

    Junjun Jiang

  3. Peng Cheng Laboratory, Shenzhen, 518000, China

    Junjun Jiang

Authors
  1. Aoxiang Fan
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  2. Xingyu Jiang
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  3. Yang Wang
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  4. Junjun Jiang
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  5. Jiayi Ma
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Corresponding author

Correspondence to Jiayi Ma .

Editor information

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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Fan, A., Jiang, X., Wang, Y., Jiang, J., Ma, J. (2020). Geometric Estimation via Robust Subspace Recovery. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-58542-6_28

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  • Online ISBN: 978-3-030-58542-6

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