gDLS: A Scalable Solution to the Generalized Pose and Scale Problem View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Chris Sweeney , Victor Fragoso , Tobias Höllerer , Matthew Turk

ABSTRACT

In this work, we present a scalable least-squares solution for computing a seven degree-of-freedom similarity transform. Our method utilizes the generalized camera model to compute relative rotation, translation, and scale from four or more 2D-3D correspondences. In particular, structure and motion estimations from monocular cameras lack scale without specific calibration. As such, our methods have applications in loop closure in visual odometry and registering multiple structure from motion reconstructions where scale must be recovered. We formulate the generalized pose and scale problem as a minimization of a least squares cost function and solve this minimization without iterations or initialization. Additionally, we obtain all minima of the cost function. The order of the polynomial system that we solve is independent of the number of points, allowing our overall approach to scale favorably. We evaluate our method experimentally on synthetic and real datasets and demonstrate that our methods produce higher accuracy similarity transform solutions than existing methods. More... »

PAGES

16-31

References to SciGraph publications

  • 2008. Automatic Generator of Minimal Problem Solvers in COMPUTER VISION – ECCV 2008
  • 2010. Location Recognition Using Prioritized Feature Matching in COMPUTER VISION – ECCV 2010
  • 2007-01. A Minimal Solution to the Generalised 3-Point Pose Problem in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • 2009-02. EPnP: An Accurate O(n) Solution to the PnP Problem in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. Exploiting Loops in the Graph of Trifocal Tensors for Calibrating a Network of Cameras in COMPUTER VISION – ECCV 2010
  • Book

    TITLE

    Computer Vision – ECCV 2014

    ISBN

    978-3-319-10592-5
    978-3-319-10593-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-10593-2_2

    DOI

    http://dx.doi.org/10.1007/978-3-319-10593-2_2

    DIMENSIONS

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