journal article·2025-08-25Open access

Analysis of the Impact of Multi-Angle Polarization Bidirectional Reflectance Distribution Function Angle Errors on Polarimetric Parameter Fusion

Zhong Lv, Zheng Qiu, Hengyi Sun, Jianwei Zhou, Jianbo Wang, Feng Chen, Haoyang Wu, Zhicheng Qin, Zhe Wang, Jingran Zhong, Yong Tan, Ye Zhang

Applied Sciences· Volume 15· Issue 17

Abstract

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Calibration and Measurement Techniques · Optical Polarization and Ellipsometry · Remote Sensing in Agriculture

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Physics, Changchun University of Science and Technology, Changchun 130022, China

School of Computer, Guangdong University of Science and Technology, Dongguan 523668, China

Funding

Jilin Province Science and Technology Development Project

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