Literature DB >> 30732279

High-speed 3D shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system.

Wei Yin, Shijie Feng, Tianyang Tao, Lei Huang, Maciej Trusiak, Qian Chen, Chao Zuo.   

Abstract

In this paper, we propose a high-speed 3D shape measurement technique based on the optimized composite fringe patterns and stereo-assisted structured light system. Stereo phase unwrapping, as a new-fashioned method for absolute phase retrieval based on the multi-view geometric constraints, can eliminate the phase ambiguities and obtain a continuous phase map without projecting any additional patterns. However, in order to ensure the stability of phase unwrapping, the period of fringe is generally around 20, which limits the accuracy of 3D measurement. To solve this problem, we develop an optimized method for designing the composite pattern, in which the speckle pattern is embedded into the conventional 4-step phase-shifting fringe patterns without compromising the fringe modulation, and thus the phase measurement accuracy. We also present a simple and effective evaluation criterion for the correlation quality of the designed speckle pattern in order to improve the matching accuracy significantly. When the embedded speckle pattern is demodulated, the periodic ambiguities in the wrapped phase can be eliminated by combining the adaptive window image correlation with geometry constraint. Finally, some mismatched regions are further corrected based on the proposed regional diffusion compensation technique (RDC). These proposed techniques constitute a complete computational framework that allows to effectively recover an accurate, unambiguous, and distortion-free 3D point cloud with only 4 projected patterns. Experimental results verify that our method can achieve high-speed, high-accuracy, robust 3D shape measurement with dense (64-period) fringe patterns at 5000 frames per second.

Year:  2019        PMID: 30732279     DOI: 10.1364/OE.27.002411

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  1 in total

1.  Temporal phase unwrapping using deep learning.

Authors:  Wei Yin; Qian Chen; Shijie Feng; Tianyang Tao; Lei Huang; Maciej Trusiak; Anand Asundi; Chao Zuo
Journal:  Sci Rep       Date:  2019-12-27       Impact factor: 4.379

  1 in total

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