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Semi-variational Registration of Range Images by Non-rigid Deformations

  • Denis Lamovsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7517)

Abstract

We present a semi-variational approach for accurate registration of a set of range images. For each range image we estimate a transformation composed of a similarity and a free-form deformation in order to obtain a smoothly stitched surface. The resulting three-dimensional model has no jumps or sharp transitions in the place of stitching. We use the presented approach for accurate human head reconstruction from a set of facets subsequently captured from different views and computed independently. A joint energy for both types of transformations is formulated, which involves several regularization constraints defined according to a specification of the resulting surface. A strategy for reweighting the impact of correspondences is presented to improve stability and convergence of the approach. We demonstrate the applicability of our method on several representative examples.

Keywords

Range Image Data Term Rigid Transformation Iterative Closed Point Data Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Denis Lamovsky
    • 1
  1. 1.FORWISSUniversität PassauPassauGermany

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