Data Driven Surface Reflectance from Sparse and Irregular Samples

Abstract

In recent years, measuring surface reflectance has become an established method for high quality renderings. In this context, especially non-parametric representations got a lot of attention as they allow for a very accurate representation of complex reflectance behavior. However, the acquisition of this data is a challenging task especially if complex object geometry is involved. Capturing images of the object under varying illumination and view conditions results in irregular angular samplings of the reflectance function with a limited angular resolution. Classical data-driven techniques, like tensor factorization, are not well suited for such data sets as they require a resampling of the high dimensional measurement data to a regular grid. This grid has to be on a much higher angular resolution to avoid resampling artifacts which in turn would lead to data sets of enormous size. To overcome these problems we introduce a novel, compact data-driven representation of reflectance functions based on a sum of separable functions which are fitted directly to the irregular set of data without any further resampling. The representation allows for efficient rendering and is also well suited for GPU applications. By exploiting spatial coherence of the reflectance function over the object a very precise reconstruction even of specular materials becomes possible already with a sparse input sampling. This would be impossible using standard data interpolation techniques. Since our algorithm exclusively operates on the compressed representation, it is both efficient in terms of memory use and computational complexity, depending only sub-linearly on the size of the fully tabulated data. The quality of the reflectance function is evaluated on synthetic data sets as ground truth as well as on real world measurements.