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. B. Goldlücke (B) Heidelberg Collaboratory for Image Processing,
Goldluecke et. al. REF tackle the problem by simultaneously optimizing the super resolved texture, the camera parameters and a surface displacement map in a global framework.
A Super-Resolution Framework for High-Accuracy Multiview Reconstruction
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science