T
Tianyang Tao
Researcher at Nanjing University of Science and Technology
Publications - 112
Citations - 3048
Tianyang Tao is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Absolute phase & Phase (waves). The author has an hindex of 20, co-authored 110 publications receiving 1787 citations. Previous affiliations of Tianyang Tao include University of Waterloo.
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Phase shifting algorithms for fringe projection profilometry: A review
TL;DR: An overview of state-of-the-art phase shifting algorithms for implementing 3D surface profilometry is presented to provide a useful guide to the selection of the most appropriate phase shifting technique for a particular application.
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Fringe pattern analysis using deep learning
TL;DR: Inspired by recent successes of deep learning techniques for computer vision and other applications, it is demonstrated for the first time that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern.
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Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second
TL;DR: A new 3D dynamic imaging technique, Micro Fourier Transform Profilometry (μFTP), which can realize an acquisition rate up to 10,000 3D frame per second (fps), and reconstruct an accurate, unambiguous, and distortion-free 3D point cloud with every two projected patterns.
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Real-time 3-D shape measurement with composite phase-shifting fringes and multi-view system
TL;DR: This work introduces a high-speed 3-D shape measurement technique based on composite phase-shifting fringes and a multi-view system that can achieve a speed of 120 frames per second with 25-period fringe patterns for fast, dense, and accurate3-D measurement.
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Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry
TL;DR: Inspired by recent successes of deep learning for FPP, this work proposes a single-shot absolute 3D shape measurement with deep-learning-based color FPP that allows for more accurate phase retrieval and more robust phase unwrapping.