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Error Analysis of SFM Under Weak-Perspective Projection

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Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3852))

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Abstract

Despite the long history of studies on weak-perspective projection and the intuitive notion that this model has better numerical stability compared to the perspective model under appropriate conditions, there lacks a deep understanding about the error characteristics of motion and depth recovery process under this model. In this paper, we present the differential approach to SFM under weak-perspective projection. Based on this approach, error behavior which governs the formation of motion ambiguities under weak-perspective is investigated. Recovery of depth information and its distortion are also discussed. Regions where ordinal depth can be extracted is shown to be following a simple relationship under all types of motion, a different result compared to the case of perspective projection.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore

    Loong-Fah Cheong & Shimiao Li

Authors
  1. Loong-Fah Cheong
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  2. Shimiao Li
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Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    P. J. Narayanan

  2. Department of Computer Science, Columbia University, 500 West 120th Street, NY 10027, New York, USA

    Shree K. Nayar

  3. Microsoft Research Asia, P.O. Box, Beijing, P.R. China

    Heung-Yeung Shum

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© 2006 Springer-Verlag Berlin Heidelberg

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Cheong, LF., Li, S. (2006). Error Analysis of SFM Under Weak-Perspective Projection. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_86

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  • DOI: https://doi.org/10.1007/11612704_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31244-4

  • Online ISBN: 978-3-540-32432-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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