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Asian Conference on Computer Vision

ACCV 2012: Computer Vision – ACCV 2012 pp 163–176Cite as

Epipolar Geometry Estimation for Urban Scenes with Repetitive Structures

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7727))

Abstract

Algorithms for the estimation of epipolar geometry from a pair of images have been very successful in recent years, being able to deal with wide baseline images. The algorithms succeed even when the percentage of correct matches from the initial set of matches is very low. In this paper the problem of scenes with repeated structures is addressed, concentrating on the common case of building facades. In these cases a large number of repeated features is found and can not be matched initially, causing state-of-the-art algorithms to fail. Our algorithm therefore clusters similar features in each of the two images and matches clusters of features. From these cluster pairs, a set of hypothesized homographies of the building facade are generated and ranked mainly according the support of matches of non-repeating features. Then in a separate step the epipole is recovered yielding the fundamental matrix. The algorithm then decides whether the fundamental matrix has been recovered reliably enough and if not returns only the homography. The algorithm has been tested successfully on a large number of pairs of images of buildings from the benchmark ZuBuD database for which several state-of-the-art algorithms nearly always fail.

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Author information

Authors and Affiliations

  1. Department of Information Systems, University of Haifa, Israel

    Maria Kushnir & Ilan Shimshoni

Authors
  1. Maria Kushnir
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  2. Ilan Shimshoni
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 151-744, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District,, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Kushnir, M., Shimshoni, I. (2013). Epipolar Geometry Estimation for Urban Scenes with Repetitive Structures. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37447-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-37447-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37446-3

  • Online ISBN: 978-3-642-37447-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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