4.5 Article

Modeling and analysis of spectral polarization BRDF based on Microfacet theory

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jqsrt.2023.108830

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Bidirectional reflectance distribution function; Polarization; Wavelength; Complex refractive index; Width parameter

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This study utilizes the polarization bidirectional reflectance distribution function (p-BRDF) model derived from the microfacet theory to analyze the spectral p-BRDF of a brass surface. The results show that factors such as polarization state, wavelength, surface roughness, and permittivity have a significant impact on the distribution of BRDF on the object surface.
The polarization bidirectional reflectance distribution function (p-BRDF) model derived from the microfacet theory is utilized for ascertaining wavelength dependent terms in the model. The spectral p-BRDF model of a given object surface is established according to the spectral models of the complex refractive index and the width parameter. Each parameter in the spectral p-BRDF model has a clear physical meaning, making it more physical than Cook-Torrance BRDF model. The particle swarm optimization algorithm combined with in-plane BRDF experimental data is used for analyzing the spectral p-BRDF of a brass surface in the visible light band. The results show that the BRDF calculated by the model is in good agreement with the experimental data, which is validated by the adjusted R2, the optimal value is 0.9872. The peak value of BRDF is found to increase with increase in wavelength for different polarization states. There exists a certain incident angle at which the BRDF initially increases and then decreases as the reflection angle is increased. The BRDF peak appears in the direction of specular reflection, and all the BRDF peaks increase with the increase in the incident angle. The polarization state, wavelength, surface roughness, and permittivity are found to be the important factors affecting the dis-tribution of BRDF on the object surface. A combination of the spectral p-BRDF model and limited experimental data related to the object surface under different testing conditions can be used to predict the spatial reflection characteristics of the surface of a given object.

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