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Article Dans Une Revue Journal of Mathematical Imaging and Vision Année : 2018

Variational Reflectance Estimation from Multi-view Images

Résumé

We tackle the problem of reectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reectance maps, through a robust vari-ational method. The variational model comprises an image-driven delity term and a term which enforces consistency of the reectance estimates with respect to each view. If illumination is xed across the views, then reectance estimation remains under-constrained: a regularization term, which ensures piecewise-smoothness of the reectance, is thus used. Reectance is pa-rameterized in the image domain, rather than on the surface, which makes the numerical solution much easier , by resorting to an alternating majorization-minimization approach. Experiments on both synthetic and real-world datasets are carried out to validate the proposed strategy.
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Dates et versions

hal-02087984 , version 1 (02-04-2019)

Identifiants

Citer

Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers. Variational Reflectance Estimation from Multi-view Images. Journal of Mathematical Imaging and Vision, 2018, 60 (9), pp.1527-1546. ⟨10.1007/s10851-018-0809-x⟩. ⟨hal-02087984⟩
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