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Journal Articles International Journal of Computer Vision Year : 2014

A Super-resolution Framework for High-Accuracy Multiview Reconstruction


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.
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Dates and versions

hal-01057502 , version 1 (22-08-2014)



Bastian Goldluecke, Mathieu Aubry, Kalin Kolev, Daniel Cremers. A Super-resolution Framework for High-Accuracy Multiview Reconstruction. International Journal of Computer Vision, 2014, 106 (2), pp.172-191. ⟨10.1007/s11263-013-0654-8⟩. ⟨hal-01057502⟩
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