Phase-shifting profilometry combined with Gray-code patterns projection has been widely used for 3D measurement. In this technique, a phase-shifting algorithm is used to calculate the wrapped phase, and a set of Gray-code binary patterns is used to determine the unwrapped phase. In the real measurement, the captured Gray-code patterns are no longer binary, resulting in phase unwrapping errors at a large number of erroneous pixels. Although this problem has been attended and well resolved by a few methods, it remains challenging when a measured object has step-heights and the captured patterns contain invalid pixels. To effectively remove unwrapping errors and simultaneously preserve step-heights, in this paper, an effective method using an adaptive median filter is proposed. Both simulations and experiments can demonstrate its effectiveness.
The three-dimensional measurement technique using binary pattern projection with projector defocusing has become increasingly important due to its high speed and high accuracy. To obtain even faster speed without sacrificing accuracy, a ternary Gray code-based phase-unwrapping method is proposed by using even fewer binary patterns, which makes it possible to efficiently and accurately unwrap the phase. Theoretical analysis, simulations, and experiments are presented to validate the proposed method's efficiency and robustness.
Phase shifting profilometry (PSP) using binary fringe patterns with projector defocusing is promising for high-speed 3D shape measurement. To obtain a high-quality phase, the projector usually requires a high defocusing level, which leads to a drastic fall in fringe contrast. Due to its convenience and high speed, PSP using squared binary patterns with small phase shifting algorithms and slight defocusing is highly desirable. In this paper, the phase accuracies of the classical phase shifting algorithms are analyzed theoretically, and then compared using both simulation and experiment. We also adapt two algorithms for PSP using squared binary patterns, which include a Hilbert three-step PSP and a double three-step PSP. Both algorithms can increase phase accuracy, with the latter featuring additional invalid point detection. The adapted algorithms are also compared with the classical algorithms. Based on our analysis and comparison results, proper algorithm selection can be easily made according to the practical requirement.
Fringe projection profilometry (FPP) has become increasingly important in dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of the measured object before shape profiling. However, traditional phase retrieval techniques often require a large number of fringes, which may generate motion-induced error for dynamic objects. In this paper, a novel phase retrieval technique based on deep learning is proposed, which uses an end-to-end deep convolution neural network to transform a single or two fringes into the phase retrieval required fringes. When the object’s surface is located in a restricted depth, the presented network only requires a single fringe as the input, which otherwise requires two fringes in an unrestricted depth. The proposed phase retrieval technique is first theoretically analyzed, and then numerically and experimentally verified on its applicability for dynamic 3-D measurement.
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