Multiple View Stereo by Reflectance Modeling

Multiple view stereo is typically formulated as an optimization problem over a data term and a prior term. The data term is based on the consistency of images projected on a hypothesized surface. This consistency is based on a measure denoted a visual metric, e.g. normalized cross correlation. Here we argue that a visual metric based on a surface reflectance model should be founded on more observations than the degrees of freedom (dof) of the reflectance model.

If (partly) specular surfaces are to be handled, this implies a model with at least two dof. In this paper, we propose to construct visual metrics of more than one dof using the DAISY methodology, which compares favorably to the state of the art in the experiments carried out. These experiments are based on a novel data set of eight scenes with diffuse and specular surfaces and accompanying ground truth.

The performance of six different visual metrics based on the DAISY framework is investigated experimentally, addressing whether a visual metric should be aggregated from a set of minimal images, which dof is best, or whether a combination of one and two dof should be used. Which metric performs best is dependent on the viewed scene, although there are clear tendencies for the two dof minimal metric to be the preferred one.