Real-time 3D shape measurement using 3LCD projection and deep machine learning

• Published in: Applied optics (2004) 2019-09, Vol.58 (26), p.7100
• Main Authors: Nguyen, Hieu, Dunne, Nicole, Li, Hui, Wang, Yuzeng, Wang, Zhaoyang
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Channels; Color imagery; Liquid crystal displays; Machine learning; Measurement techniques; Projectors; Real time; Three dimensional imaging; Three dimensional models
• ISSN: 1559-128X; 2155-3165
• DOI: 10.1364/AO.58.007100

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.