International Journal of Computer Vision

, Volume 106, Issue 2, pp 172–191 | Cite as

A Super-Resolution Framework for High-Accuracy Multiview Reconstruction

  • Bastian GoldlückeEmail author
  • Mathieu Aubry
  • Kalin Kolev
  • Daniel Cremers


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.


Multi-view 3D reconstruction Texture reconstruction Super-resolution Camera calibration Variational methods 



We thank Martin R. Oswald for providing the visualization in Fig. 1. This work was supported by the ERC Starting Grant “Convex Vision”.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bastian Goldlücke
    • 1
    Email author
  • Mathieu Aubry
    • 2
  • Kalin Kolev
    • 3
  • Daniel Cremers
    • 2
  1. 1.Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergHeidelbergGermany
  2. 2.Computer Science DepartmentTechnical University of MunichMunichGermany
  3. 3.Department of Computer ScienceETH ZurichZurichSwitzerland

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