A Super-resolution Framework for High-Accuracy Multiview Reconstruction

Bastian Goldluecke 1 Mathieu Aubry 2, 3, 4, 5 Kalin Kolev 6 Daniel Cremers 7
1 HCI - Heidelberg Collaboratory for Image Processing
2 ENPC - École des Ponts ParisTech
3 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
4 WILLOW - Models of visual object recognition and scene understanding
CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique de l'École normale supérieure
5 LIGM - Laboratoire d'Informatique Gaspard-Monge
6 Computer Vision Laboratory - ETHZ [Zurich]
7 CVPR - Computer Vision and Pattern Recognition Group [Münster]
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.
Keywords : Variational methods Super-resolution Camera calibration Multi-view 3D reconstruction Texture reconstruction
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Bastian Goldluecke, Mathieu Aubry, Kalin Kolev, Daniel Cremers. A Super-resolution Framework for High-Accuracy Multiview Reconstruction. International Journal of Computer Vision, Springer Verlag, 2014, 106 (2), pp.172-191. ⟨10.1007/s11263-013-0654-8⟩. ⟨hal-01057502⟩

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