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• W1970260218 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." @default.
• W1970260218 created "2016-06-24" @default.
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• W1970260218 date "2013-08-25" @default.
• W1970260218 modified "2023-09-25" @default.
• W1970260218 title "A Super-Resolution Framework for High-Accuracy Multiview Reconstruction" @default.
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• W1970260218 doi "https://doi.org/10.1007/s11263-013-0654-8" @default.
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