Multi-band Polarization Imaging and Applications

A polarimetric bidirectional reflectance distribution function (pBRDF), based on geometrical optics, is presented. The pBRDF incorporates a visibility (shadowing/masking) function and a Lambertian (diffuse) component which distinguishes it from other geometrical optics pBRDFs in literature. It is shown that these additions keep the pBRDF bounded (and thus a more realistic physical model) as the angle of incidence or observation approaches grazing and better able to model the behavior of light scattered from rough, reflective surfaces. In this paper, the theoretical development of the pBRDF is shown and discussed. Simulation results of a rough, perfect reflecting surface obtained using an exact, electromagnetic solution and experimental Mueller matrix results of two, rough metallic samples are presented to validate the pBRDF.

/pdf/a-geometrical-optics-polarimetric-bidirectional-reflectance-qmttxtsqe7.pdf

A geometrical optics polarimetric bidirectional reflectance distribution function for dielectric and metallic surfaces

Capturing appearance often requires dense sampling in light-view space, which is often achieved in specialized, expensive hardware setups. With the aim of realizing a compact acquisition setup without multiple angular samples of light and view, we sought to leverage an alternative optical property of light, polarization. To this end, we capture a set of polarimetric images with linear polarizers in front of a single projector and camera to obtain the appearance and normals of real-world objects. We encountered two technical challenges: First, no complete polarimetric BRDF model is available for modeling mixed polarization of both specular and diffuse reflection. Second, existing polarization-based inverse rendering methods are not applicable to a single local illumination setup since they are formulated with the assumption of spherical illumination. To this end, we first present a complete polarimetric BRDF (pBRDF) model that can define mixed polarization of both specular and diffuse reflection. Second, by leveraging our pBRDF model, we propose a novel inverse-rendering method with joint optimization of pBRDF and normals to capture spatially-varying material appearance: per-material specular properties (including the refractive index, specular roughness and specular coefficient), per-pixel diffuse albedo and normals. Our method can solve the severely ill-posed inverse-rendering problem by carefully accounting for the physical relationship between polarimetric appearance and geometric properties. We demonstrate how our method overcomes limited sampling in light-view space for inverse rendering by means of polarization.

Simultaneous acquisition of polarimetric SVBRDF and normals

This paper is focused on the Bidirectional Reflectance Distribution Function (BRDF) in the context of algorithms for computational production of realistic synthetic images. We provide a review of most relevant analytical BRDF models proposed in the literature which have been used for realistic rendering. We also show different approaches used for obtaining efficient models from acquired reflectance data, and the related function fitting techniques, suitable for using that data in efficient rendering algorithms. We consider the algorithms for computation of BRDF integrals, by using Monte-Carlo based numerical integration. In this context, we review known techniques to design efficient BRDF sampling schemes for both analytical and measured BRDF models.

An Overview of BRDF Models

Polarization adds another dimension to the spatial intensity and spectral information typically acquired in remote sensing. Polarization imparted by surface reflections contains unique and discriminatory signatures which may augment spectral target-detection techniques. While efforts have been made toward quantifying the polarimetric bidirectional reflectance distribution function (pBRDF) responsible for target material polarimetric signatures, little has been done toward developing a description of the polarized background or scene clutter. An approach is presented for measuring the pBRDF of background materials such as vegetation. The governing equation for polarized radiance reaching a sensor aperture is first developed and serves as a basis for understanding outdoor pBRDF measurements, as well as polarimetric remote sensing. The pBRDF measurements are acquired through an imaging technique which enables derivation of the BRDF variability as a function of the ground separation distance (GSD). An image subtraction technique is used to minimize measurement errors resulting from the partially polarized downwelled sky radiance. Quantifying the GSD-dependent BRDF variability is critical for background materials which are typically spatially inhomogeneous. Preliminary results from employing the measurement technique are presented.

Polarimetric Remote Sensing in the Visible to Near Infrared

: This report describes a method for using bidirectional reflectance information previously reported in the Eleventh Supplement to the Target Signature Analysis Center: Data Compilation and further validates the bidirectional reflectance model originated and extended under recent contracts. It includes bidirectional reflectance model parameters for a variety of paints. Parameters were extracted from measurement data reported in the Eleventh Supplement. Reduced reflectance data are also provided; these data may be used with the computer model or, optionally, in an interpolation procedure for estimating reflectances without the aid of a computer. The computer model makes it possible to calculate bidirectional reflectance data from a very small amount of measured data. Accuracy demonstrated in the Model Validation section indicates that the model is very effective, although improvement can still be obtained at large receiver zenith angles. The interpolation procedure also shows excellent agreement with measurement. (Author)

/pdf/bidirectional-reflectance-model-validation-and-utilization-4h2cy9j23d.pdf

Bidirectional Reflectance Model Validation and Utilization.

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Bidirectional Reflectance Model Validation and Utilization.