IN-LABORATORY SIMPLIFIED IMAGE-BASED EMPIRICAL POLARIMETRIC BIDIRECTIONAL REFLECTANCE DISTRIBUTION FUNCTION MEASUREMENT USING 3D GEOMETRIC TARGETS

Abstract

The bidirectional reflectance distribution function (BRDF) is essential to remote sensing, computer graphics, and material science applications. It aids the development of photo-realistic rendering models, target detection and recognition, and atmospheric characterization tasks in remote sensing. BRDF measurements are cumbersome to make, requiring dense, full hemispherical sampling that often results in millions of individual measurements conducted at precise sensor and illumination source geometries. Methods have been proposed based upon theoretical, experimental, and empirical approaches that aim to simplify the data collection process. Empirically, researchers have proposed using targets of different geometric shapes in conjunction with image-based sensors to acquire measurements in parallel, thus reducing the acquisition time. This work surveys studies of such proposed methods to measure BRDF/polarimetric BRDF (pBRDF). Our group previously presented a fast and simple framework for empirically measuring the pBRDF using a linear imaging polarimeter from novel 3D-printed geodesic target spheres with well-characterized surface facets under outdoor environmental conditions. The models derived from this approach were validated against physics-based models and demonstrated good agreement. In this work, we present a modified approach to conduct similar measurements on the same faceted objects in a laboratory environment. The Applied Sensing Lab at the University of Dayton has constructed a solar simulation laboratory that allows for highly accurate and repeatable positioning of light sources, sensors, and objects. The laboratory contains both collimated (direct sun) and diffuse (downwelling) light sources that we have spectrally tuned in this work to match expected solar irradiance under a range of outdoor conditions. The laboratory-based pBRDF models obtained by our proposed framework validate strongly against their corresponding outdoor, spectroradiometric measurements (ground truth), and physics-based models.