  We tackle the problem of reflectance estimation from a set of multi-view
images, assuming known geometry. The approach we put forward turns the input
images into reflectance maps, through a robust variational method. The
variational model comprises an image-driven fidelity term and a term which
enforces consistency of the reflectance estimates with respect to each view. If
illumination is fixed across the views, then reflectance estimation remains
under-constrained: a regularization term, which ensures piecewise-smoothness of
the reflectance, is thus used. Reflectance is parameterized 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 datasets are carried out to validate the
proposed strategy.
