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Volume 25
Issue 20
10.3390/s25206296
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration

by
Zhongyuan Zhang
1,2,
Hao Wang
1,2,
Yiming Li
2,
Zinan Li
1,
Weihua Gui
2,3,
Xiaohao Wang
1,
Chaobo Zhang
2,
Xiaojun Liang
2,* and
Xinghui Li
1,*
1
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China
2
Pengcheng Laboratory, Shenzhen 518000, China
3
School of Automation, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Sensors 2025, 25(20), 6296; https://doi.org/10.3390/s25206296 (registering DOI)
Submission received: 12 August 2025 / Revised: 2 October 2025 / Accepted: 8 October 2025 / Published: 11 October 2025
(This article belongs to the Section Sensing and Imaging)
Versions Notes

Abstract

Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation.
Keywords: fringe structured light; fringe projection profilometry; phase measuring deflectometry; deep learning; 3D measurement fringe structured light; fringe projection profilometry; phase measuring deflectometry; deep learning; 3D measurement

Share and Cite

MDPI and ACS Style

Zhang, Z.; Wang, H.; Li, Y.; Li, Z.; Gui, W.; Wang, X.; Zhang, C.; Liang, X.; Li, X. Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration. Sensors 2025, 25, 6296. https://doi.org/10.3390/s25206296

AMA Style

Zhang Z, Wang H, Li Y, Li Z, Gui W, Wang X, Zhang C, Liang X, Li X. Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration. Sensors. 2025; 25(20):6296. https://doi.org/10.3390/s25206296

Chicago/Turabian Style

Zhang, Zhongyuan, Hao Wang, Yiming Li, Zinan Li, Weihua Gui, Xiaohao Wang, Chaobo Zhang, Xiaojun Liang, and Xinghui Li. 2025. "Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration" Sensors 25, no. 20: 6296. https://doi.org/10.3390/s25206296

APA Style

Zhang, Z., Wang, H., Li, Y., Li, Z., Gui, W., Wang, X., Zhang, C., Liang, X., & Li, X. (2025). Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration. Sensors, 25(20), 6296. https://doi.org/10.3390/s25206296

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