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

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Supplementary Material (3)

NameDescription
» Visualization 1       Illustration of the 3LCD projection mechanism
» Visualization 2       Demonstration of real-time 3D shape measurement
» Visualization 3       Demonstration of 360-degree reconstructed models

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