Hot Paper - Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects

Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects

深度学习使分离物体的高速复合条纹模式轮廓术中的单次绝对相位恢复成为可能
深さ学習は、分離物体の高速複合ストライプパターン輪郭術における単回絶対位相回復を可能にする
딥 러닝은 물체를 분리하는 고속 복합 줄무늬 모드 윤곽술에서의 단일 절대 위상 회복을 가능하게 한다
El aprendizaje profundo hace posible la recuperación de fase absoluta única en la silueta de modo rayado compuesto de alta velocidad de objetos separados
L'apprentissage en profondeur permet une récupération de phase absolue unique en contournage à grande vitesse en mode strié composite d'objets séparés
Глубокое обучение делает возможным однократное абсолютное восстановление фазы в высокоскоростном композитном полосовом режиме для отдельных объектов

Maciej Trusiak, Malgorzata Kujawinska

Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli Street, Warsaw 02-525, Poland

https://doi.org/10.29026/oea.2023.230172

https://www.oejournal.org/article/doi/10.29026/oea.2023.230172

Opto-Electronic Advances, 12 December 2023

Abstract

A recent article in the Opto-Electronic Advances (OEA) journal from Prof. Qian Chen and Prof. Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry (FPP).

In this News & Views article, we explore potential avenues for future advancements, including expanding the measurement range through an extended number-theoretical approach, enhancing quality through the incorporation of horizontal fringes, and integrating data from other modalities to broaden the system's applications.

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