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Three-dimensional shape measurement method based on composite cyclic phase coding.

Zicong Zou, Yongjian Zhu, Guofeng Qin

|January 6, 2023

Summary

This summary is machine-generated.

This study introduces composite cyclic phase coding for 3D shape measurement, enhancing accuracy by expanding codewords. The method offers robust, high-precision 3D reconstruction, overcoming limitations of traditional phase coding techniques.

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Area of Science:

Optics and Photonics
Computer Vision
Metrology

Background:

Phase coding is a robust technique for 3D measurement but faces accuracy limitations due to restricted codeword numbers.
Traditional methods struggle with high-frequency fringes, system nonlinearity, and noise, leading to misjudgments.

Purpose of the Study:

To propose a novel 3D shape measurement method using composite cyclic phase coding.
To overcome the codeword limitation in traditional phase coding for improved measurement accuracy.
To enhance robustness and reduce algorithm complexity in 3D shape reconstruction.

Main Methods:

Developed a composite cyclic phase coding strategy by quantizing cyclic patterns and using grayscale values to differentiate codewords.
Integrated composite cyclic phase coding into a sequential fringe order for phase unwrapping.
Expanded the number of available codewords without adding extra patterns.

Main Results:

Achieved stable and high-accuracy 3D reconstruction, surpassing traditional phase coding limitations.
Successfully overcame codeword misjudgments caused by system nonlinearity and noise under high-frequency fringes.
Demonstrated superior performance compared to methods combining temporal and spatial domain information, such as quantized phase coding and connected region labeling.

Conclusions:

Composite cyclic phase coding effectively expands codewords, enabling high-accuracy 3D shape measurement.
The proposed method exhibits high robustness and low algorithm complexity, avoiding error propagation issues.
This technique provides a reliable solution for challenging 3D measurement scenarios involving nonlinearity and noise.