A Superresolution Framework for High-Accuracy Multiview Reconstruction

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2014
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Aubry, Mathieu
Kolev, Kalin
Cremers, Daniel
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International Journal of Computer Vision. 2014, 106(2), pp. 172-191. ISSN 0920-5691. eISSN 1573-1405. Available under: doi: 10.1007/s11263-013-0654-8
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Multi-view 3D reconstruction, Texture reconstruction, Super-resolution, Camera calibration, Variational methods
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ISO 690GOLDLÜCKE, Bastian, Mathieu AUBRY, Kalin KOLEV, Daniel CREMERS, 2014. A Superresolution Framework for High-Accuracy Multiview Reconstruction. In: International Journal of Computer Vision. 2014, 106(2), pp. 172-191. ISSN 0920-5691. eISSN 1573-1405. Available under: doi: 10.1007/s11263-013-0654-8
BibTex
@article{Goldlucke2014Super-29111,
  year={2014},
  doi={10.1007/s11263-013-0654-8},
  title={A Superresolution Framework for High-Accuracy Multiview Reconstruction},
  number={2},
  volume={106},
  issn={0920-5691},
  journal={International Journal of Computer Vision},
  pages={172--191},
  author={Goldlücke, Bastian and Aubry, Mathieu and Kolev, Kalin and Cremers, Daniel},
  note={received DAGM main prize (best paper award)}
}
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received DAGM main prize (best paper award)
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