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A Super-resolution Framework for High-Accuracy Multiview Reconstruction
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.
Cited literature [45 references]
https://hal.inria.fr/hal-01057502
Contributor : Mathieu Aubry <>
Submitted on : Friday, August 22, 2014 - 5:44:20 PM
Last modification on : Wednesday, February 3, 2021 - 7:54:27 AM
Long-term archiving on: : Thursday, November 27, 2014 - 1:56:46 PM
Contributor : Mathieu Aubry <>
Submitted on : Friday, August 22, 2014 - 5:44:20 PM
Last modification on : Wednesday, February 3, 2021 - 7:54:27 AM
Long-term archiving on: : Thursday, November 27, 2014 - 1:56:46 PM
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GAKC13_ijcv.pdf
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