Abstract
For 3D imaging and shape measurement, simultaneously achieving real-time and high-accuracy performance remains a challenging task in practice. In this paper, a fringe-projection-based 3D imaging and shape measurement technique using a three-chip liquid-crystal-display (3LCD) projector and a deep machine learning scheme is presented. By encoding three phase-shifted fringe patterns into the red, green, and blue (RGB) channels of a color image and controlling the 3LCD projector to project the RGB channels individually, the technique can synchronize the projector and the camera to capture the required fringe images at a fast speed. In the meantime, the 3D imaging and shape measurement accuracy is dramatically improved by introducing a novel phase determination approach built on a fully connected deep neural network (DNN) learning model. The proposed system allows performing 3D imaging and shape measurement of multiple complex objects at a real-time speed of 25.6 fps with relative accuracy of 0.012%. Experiments have shown great promise for advancing scientific and engineering applications.
© 2019 Optical Society of America
Full Article | PDF ArticleMore Like This
Hieu Nguyen, Dung Nguyen, Zhaoyang Wang, Hien Kieu, and Minh Le
Appl. Opt. 54(1) A9-A17 (2015)
Yueyang Li, Zhouejie Wu, Junfei Shen, and Qican Zhang
Opt. Express 31(24) 40803-40823 (2023)
Andrew-Hieu Nguyen, Khanh L. Ly, Charlotte Qiong Li, and Zhaoyang Wang
Appl. Opt. 61(29) 8589-8599 (2022)