Experimental and Theoretical Basis for a Closed-Form Spectral BRDF Model

The microfacet class of BRDF models is frequently used to calculate optical scatter from realistic surfaces using geometric optics, but has the disadvantage of not being able to consider wavelength dependence. This dissertation works toward development of a closed-form approximation to the BRDF that is suitable for hyperspectral remote sensing by presenting measured BRDF data of 12 different materials at four different incident angles and up to seven different wavelengths between 3.39 and 10.6 micrometer. The data was intended to be fit to various microfacet BRDF models to determine an appropriate form of the wavelength scaling. However, when fitting the microfacet models to measured data, the results indicated a breakdown in the microfacet model itself. To overcome this deficiency, elements of microfacet BRDF models are compared to elements of scalar wave optics BRDF models, which inherently contain a wavelength dependence. This analysis led to a theoretical understanding of how to modify microfacet BRDF models to maintain the simplicity of a closed-form model, while better approximating the underlying physics

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

A Dual-Beam Method-of-Images 3D Searchlight BSSRDF

We present a novel BSSRDF for rendering translucent materials. Angular effects lacking in previous BSSRDF models are incorporated by using a dual-beam formulation. We employ a Placzek's Lemma interpretation of the method of images and discard diffusion theory. Instead, we derive a plane-parallel transformation of the BSSRDF to form the associated BRDF and optimize the image confiurations such that the BRDF is close to the known analytic solutions for the associated albedo problem. This ensures reciprocity, accurate colors, and provides an automatic level-of-detail transition for translucent objects that appear at various distances in an image. Despite optimizing the subsurface fluence in a plane-parallel setting, we find that this also leads to fairly accurate fluence distributions throughout the volume in the original 3D searchlight problem. Our method-of-images modifications can also improve the accuracy of previous BSSRDFs.Comment: added clarifying text and 1 figure to illustrate the metho