A super-resolution framework for high-accuracy multiview reconstruction

Bastian Goldlücke, Mathieu Aubry, Kalin Kolev, Daniel Cremers

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


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.

Original languageEnglish
Pages (from-to)172-191
Number of pages20
JournalInternational Journal of Computer Vision
Issue number2
StatePublished - Jan 2014


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


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