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Beyond Feature Points: Structured Prediction for Monocular Non-rigid 3D Reconstruction

  • Mathieu Salzmann
  • Raquel Urtasun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

Existing approaches to non-rigid 3D reconstruction either are specifically designed for feature point correspondences, or require a good shape initialization to exploit more complex image likelihoods. In this paper, we formulate reconstruction as inference in a graphical model, where the variables encode the rotations and translations of the facets of a surface mesh. This lets us exploit complex likelihoods even in the absence of a good initialization. In contrast to existing approaches that set the weights of the likelihood terms manually, our formulation allows us to learn them from as few as a single training example. To improve efficiency, we combine our structured prediction formalism with a gradient-based scheme. Our experiments show that our approach yields tremendous improvement over state-of-the-art gradient-based methods.

Keywords

Feature Point Structure Prediction Mesh Vertex Reprojection Error Pairwise Potential 
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

  • Mathieu Salzmann
    • 1
  • Raquel Urtasun
    • 2
  1. 1.NICTAAustralia
  2. 2.TTI ChicagoUSA

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