A Super-Resolution Framework for High-Accuracy Multiview Reconstruction

  • Authors:

  • Bastian Goldlücke;Mathieu Aubry;Kalin Kolev;Daniel Cremers

  • Affiliations:

  • Heidelberg Collaboratory for Image Processing, University of Heidelberg, Heidelberg, Germany;Computer Science Department, Technical University of Munich, Munich, Germany;Department of Computer Science, ETH Zurich, Zurich, Switzerland;Computer Science Department, Technical University of Munich, Munich, Germany

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2014

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Abstract

We present a variational framework to estimate super-resolved texture maps on a 3D geometry model of a surface from multiple images. Given the calibrated images and the reconstructed geometry, the proposed functional is convex in the super-resolution texture. Using a conformal atlas of the surface, we transform the model from the curved geometry to the flat charts and solve it using state-of-the-art and provably convergent primal---dual algorithms. In order to improve image alignment and quality of the texture, we extend the functional to also optimize for a normal displacement map on the surface as well as the camera calibration parameters. Since the sub-problems for displacement and camera parameters are non-convex, we revert to relaxation schemes in order to robustly estimate a minimizer via sequential convex programming. Experimental results confirm that the proposed super-resolution framework allows to recover textured models with significantly higher level-of-detail than the individual input images.