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Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence

  • Matthias VestnerEmail author
  • Emanuele Rodolà
  • Thomas Windheuser
  • Samuel Rota Bulò
  • Daniel Cremers
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We introduce a novel dense shape matching method for deformable, three-dimensional shapes. Differently from most existing techniques, our approach is general in that it allows the shapes to undergo deformations that are far from being isometric. We do this in a supervised learning framework which makes use of training data as represented by a small set of example shapes. From this set, we learn an implicit representation of a shape descriptor capturing the variability of the deformations in the given class. The learning paradigm we choose for this task is a random forest classifier. With the additional help of a spatial regularizer, the proposed method achieves significant improvements over the baseline approach and obtains state-of-the-art results while keeping a low computational cost.

Keywords

Random Forest Shape Descriptor Split Function Shape Match Random Forest Classifier 
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 International Publishing Switzerland 2016

Authors and Affiliations

  • Matthias Vestner
    • 1
    Email author
  • Emanuele Rodolà
    • 1
  • Thomas Windheuser
    • 1
  • Samuel Rota Bulò
    • 2
  • Daniel Cremers
    • 1
  1. 1.Technische Universität MünchenMunichGermany
  2. 2.Fondazione Bruno KesslerTrentoItaly

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