The accuracy in the simulation of scattering at surfaces and computation of the light transport in a scene is determined by the way the material properties such as color and reflectance, modeled by the Bidirectional Reflectance Distribution Function (BRDF), are measured and represented. Over the last decade, we have seen the development of computer graphics algorithms and techniques which enable the quality and accuracy needed to make rendered images truly comparable to photographs of the same scene. To demonstrate the benefit of the proposed factored models, we present a new Monte Carlo importance sampling scheme and give examples of how they can be used for efficient BRDF capture and intuitive editing of measured materials.
We evaluate the models using different parameterizations with different characteristics and show that both the BRDF data itself and the resulting renderings yield more accurate results in terms of both numerical errors and visual results compared to previous approaches. The proposed 3D and 2D BRDF representations can be factored into three or two 1D factors, respectively, while accurately representing the underlying BRDF data with only small approximation error.
This paper presents two new data-driven BRDF models specifically designed for 1D separability. There are, however, a number of key challenges that need to be solved in order to enable efficient capture, representation and interaction with real materials. The reason is that data-driven models can easily capture the underlying, fine details that represent the visual appearance of materials, which can be difficult or even impossible to model by hand.
Measured materials are rapidly becoming a core component in the photo-realistic image synthesis pipeline.
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