Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review

By Chao Zuo, Lei Huang, Minliang Zhang, Qian Chen, Anand K. Asundi. 2016

891 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/5c3d6f157d37079cb9d1309b55d5b457c2fe2387

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. Tianyang Tao
2. Guohua Gu
3. Chao Zuo
4. Yixuan Li
5. Kemao Qian
6. Pengfei Fan
7. Wei Yin
8. Liang Zhang

In Mechanical Engineering

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

Citations in the corpus

• Fringe pattern analysis using deep learning (2018, methodology): “To demonstrate the accuracy of determined phase, we unwrapped it by multi-frequency temporal phase unwrapping 11 and calculated the phase error against the result recovered by 12-step phase-shifting method.”
• Deep learning in optical metrology: a review (2022, methodology): “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 evaluatio”