The bidirectional reflectance distribution function (BRDF) is a fitted distribution function that defines the scatter of light
off of a surface. The BRDF is dependent on the directions of both the incident and scattered light. Because of the
vastness of the measurement space of all possible incident and reflected directions, the calculation of BRDF is usually
performed using a minimal amount of measured data. This may lead to poor fits and uncertainty in certain regions of
incidence or reflection. A dynamic data driven application system (DDDAS) is a concept that uses an algorithm on
collected data to influence the collection space of future data acquisition. The authors propose a DDD-BRDF algorithm
that fits BRDF data as it is being acquired and uses on-the-fly fittings of various BRDF models to adjust the potential
measurement space. In doing so, it is hoped to find the best model to fit a surface and the best global fit of the BRDF
with a minimum amount of collection space.
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