Variational Reflectance Estimation from Multi-view Images
This addresses reflectance estimation for computer vision applications, but it is incremental as it builds on existing variational frameworks with a parameterization change.
The paper tackles reflectance estimation from multi-view images with known geometry using a variational method that enforces consistency across views and piecewise-smoothness regularization, validated on synthetic and real datasets.
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.