Phase shifting algorithms for fringe projection profilometry: A review

By Chao Zuo, Shijie Feng, Lei Huang, Tianyang Tao, Wei Yin, Qian Chen. Optics and lasers in engineering, 2018

1,097 citations recorded by the source corpus.

Abstract The principle of structured light and triangulation is used in a wide range of 3D optical metrology applications, such as mechanical engineering, industrial monitoring, computer vision, and biomedicine. Among a multitude of techniques based on this principle, phase shifting profilometry (PSP) plays a dominant role due to its high attainable measurement accuracy, spatial resolution, and data density. Over the past few decades, many PSP algorithms have been proposed in the literature in order to achieve higher measurement accuracy, lower pattern count, and/or better robustness to different error sources. Besides, many unconventional PSP codification techniques address the problem of absolute phase recovery with few projected patterns, allowing for high-efficiency measurement of objects containing isolated regions or surface discontinuities. In this paper, we present an overview of these state-of-the-art phase shifting algorithms for implementing 3D surface profilometry. Typical error sources in phase measurement for a phase shifting system are discussed, and corresponding solutions are reviewed. The advantages and drawbacks of different PSP algorithms are also summarized to provide a useful guide to the selection of the most appropriate phase shifting technique for a particular application.

Subfields: Computer Vision and Pattern Recognition, Mechanical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/ce651c65d64546f6155883a3d348860d34de090a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chao Zuo
2. Yixuan Li
3. Kemao Qian
4. Pengfei Fan
5. Wei Yin
6. Jing Han
7. Shijie Feng
8. Qian Chen

In Mechanical Engineering

1. Chao Zuo
2. Wei Yin
3. Shijie Feng
4. Qian Chen
5. Bing Pan

In Media Technology

1. Chao Zuo
2. Wei Yin
3. Shijie Feng
4. Qian Chen
5. Song Zhang
6. Bing Pan

Citations in the corpus

• Fringe pattern analysis using deep learning (2018, neutral): “…can be broadly classified into two categories: (1) phase-shifting (PS) methods that require multiple fringe patterns to extract phase information, 4 and (2) spatial phase-demodulation methods that allow phase retrieval from a single fringe pattern, such as the Fourier transform (FT), 5 windowed…”
• Deep learning in optical metrology: a review (2022, background): “For more detailed information about these methods, the reader can refer to the comparative review by Zuo et al.174 The advantage of temporal phase unwrapping lies in that the unwrapping is neighborhood-independent and proceeds along the time axis on the pixel itself, enabling an absolute evaluation ”