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Quasi-Perspective Factorization

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Guide to Three Dimensional Structure and Motion Factorization

Part of the book series: Advances in Pattern Recognition ((ACVPR))

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Abstract

Previous studies on structure and motion factorization are either based on simplified affine assumption or general perspective projection. The affine approximation is widely adopted due to its simplicity, whereas the extension to perspective model suffers from difficulties in projective depth recovery. To fill the gap between simplicity of affine and accuracy of perspective model, we propose a quasi-perspective factorization algorithm for structure and motion recovery of both rigid and nonrigid objects. Firstly, we establish a framework of rigid and nonrigid factorization under quasi-perspective assumption. Secondly, we propose an extended Cholesky decomposition to recover the rotation part of the Euclidean upgrading matrix. Finally, we prove that the last column of the upgrading matrix corresponds to a global scale and translation of the camera thus may be set freely. The proposed algorithm is validated and evaluated extensively on synthetic and real image sequences.

Each problem that I solved became a rule which served afterwards to solve other problems.

René Descartes (1596–1650)

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Authors and Affiliations

    Authors
    1. Guanghui Wang
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    2. Q. M. Jonathan Wu
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    Correspondence to Guanghui Wang or Guanghui Wang .

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    © 2011 Springer-Verlag London Limited

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    Wang, G., Wu, Q.M.J. (2011). Quasi-Perspective Factorization. In: Guide to Three Dimensional Structure and Motion Factorization. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-046-5_9

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    • DOI: https://doi.org/10.1007/978-0-85729-046-5_9

    • Publisher Name: Springer, London

    • Print ISBN: 978-0-85729-045-8

    • Online ISBN: 978-0-85729-046-5

    • eBook Packages: Computer ScienceComputer Science (R0)

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