| Authors |
K. Kolev, Pock Thomas, D. Cremers |
| Appeared in |
European Conference on Computer Vision (ECCV)
|
| Date |
2010 |
| Abstract |
In this work the weighted minimal surface model tradition-
ally used in multiview stereo is revisited. We propose to generalize the
classical photoconsistency-weighted minimal surface approach by means
of an anisotropic metric which allows to integrate a specified surface
orientation into the optimization process. In contrast to the conven-
tional isotropic case, where all spatial directions are treated equally, the
anisotropic metric adaptively weights the regularization along different
directions so as to favor certain surface orientations over others. We show
that the proposed generalization preserves all properties and globality
guarantees of continuous convex relaxation methods. We make use of a
recently introduced efficient primal-dual algorithm to solve the arising
saddle point problem. In multiple experiments on real image sequences
we demonstrate that the proposed anisotropic generalization allows to
overcome oversmoothing of small-scale surface details, giving rise to more
precise reconstructions.
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| Link |
URL
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