A Super-Resolution Framework for High-Accuracy Multiview Reconstruction.

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Bibliographic Details
Title:	A Super-Resolution Framework for High-Accuracy Multiview Reconstruction.
Authors:	Goldlücke, Bastian¹ bastian.goldluecke@iwr.uni-heidelberg.de, Aubry, Mathieu² mathieu.aubry@in.tum.de, Kolev, Kalin³ kalin.kolev@inf.ethz.ch, Cremers, Daniel² cremers@tum.de
Source:	International Journal of Computer Vision. Jan2014, Vol. 106 Issue 2, p172-191. 20p. 10 Color Photographs, 5 Diagrams, 3 Charts, 1 Graph.
Subjects:	Geometry problems & exercises, Graphic methods, Mathematical optimization, Maps, Three-dimensional imaging
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	09205691
DOI:	10.1007/s11263-013-0654-8