A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparel. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we propose a laser-based surface-profiling system that utilizes a smart camera to sense the 3-D topography of fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. We detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system between two studies that total more than 200 fabric specimens. The results show that with the features used so far, this system is at least as good as the current American Association of Textile Chemists and Colorists (AATCC) smoothness grading system.