BRDF modeling and optimization of a target surface based on the gradient descent algorithm

被引:2
|
作者
Li, Yanhui [1 ]
Yang, Pengfei [1 ]
Bai, Lu [1 ]
Zhang, Zifei [1 ]
机构
[1] Xidian Univ, Sch Phys, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
10.1364/AO.506672
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Addressing the current challenges in modeling and optimizing the bidirectional reflectance distribution function (BRDF) for the target surface, an improved six-parameter semi-empirical model is proposed based on an existing five-parameter semi-empirical model. In comparison with the original five-parameter model, the new, to the best of our knowledge, model considers reciprocity, and the results demonstrate that as the incident angle increases, the fitting accuracy of the six parameters gradually surpasses that of the five parameters. Additionally, this paper employs a machine learning optimization algorithm, namely, the gradient descent method, for optimizing the BRDF. The algorithm was comprehensively compared with other optimization methods, revealing that for the same dataset, the gradient descent method exhibited the smallest fitting errors. Subsequently, utilizing this algorithm for fitting experimental data resulted in errors consistently within 3%, confirming the reliability and accuracy of this optimization algorithm.(c) 2023 Optica Publishing Group
引用
收藏
页码:9486 / 9492
页数:7
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