Mr. Mu J. Li Profile

Mu J. Li

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Proceedings Article | 5 December 2024 Paper

A new fringe analysis method based on shared bottom network for single frame 3D reconstruction

Jin Lu, Min Jie Zhang, Mu H. Li, Mu J. Li, Jin Wang, Pei Y. Wu, Wei Sun, Lin Li

Proceedings Volume 13418, 1341838 (2024) https://doi.org/10.1117/12.3048685

KEYWORDS: Fringe analysis, Feature extraction, Convolution, Semantics, Phase unwrapping, Network architectures, Phase shifts, Deep learning, 3D modeling, Projection systems

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Recently, deep learning-based methods have been employed in optical measurement. The fringe to phase method based on deep learning can achieve high-precision 3D topography measurement and is applied to various optical metrology tasks, including phase extraction, phase unwrapping, fringe order determination, depth estimation, and other crucial steps. However, it appears simplistic to obtain images of each metrological task from a single fringe pattern. This paper proposes a novel network that effectively extracts the semantic features of fringe patterns by incorporating the design architecture of transformer while retaining the advantages of convolutional networks. The architecture primarily consists of a backbone, decoder, and feature extraction block which enhance the features at different frequencies within a single fringe pattern. The backbone and decoder are specifically designed for wrapped phase prediction tasks. Experimental results demonstrate that the network accurately predicts the wrapped phase from a single fringe pattern. In comparison with previous methods, this paper's approach offers several contributions: an efficient utilization of a new type of encoder for extracting high-level semantic features from fringe patterns; moreover, only a single grayscale image is required as input for the network without relying on color composite images or additional prior information.

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