Phase Error Analysis and Correction for Crossed-Grating Phase-Shifting Profilometry

Li, Fuqian; Chen, Wenjing

doi:10.3390/s21196475

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…Mu˜noz et al [20] indirectly determines the gamma factor by obtaining the estimated phase of the reference plane through the least-squares method. Li and Chen [21] proposes a mathematical model-based doublefive-step algorithm in combination with the cross-grid phaseshifting contouring technique to suppress nonlinearities. The method achieves fast and reliable reverse pattern projection with fewer fringe patterns compared to projecting 24 and 18 patterns.…”

Section: Introductionmentioning

confidence: 99%

The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function

Zhu,

Xu,

Long

et al. 2024

Meas. Sci. Technol.

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In a digital stripe projection structured light system, the nonlinear phase error is generated by the gamma effect of both the projector, camera, and other electronic devices. One of the existing nonlinear correction methods is active correction by projecting ideal fringes as far as possible, and the other is passive compensation after capturing aberrant fringes. The former has higher accuracy but needs to capture a large number of fringe patterns, while the latter does not need many fringe patterns, but is not only greatly affected by random noise and out-of-focus effects, but also has poor accuracy. In this paper, an optimal algorithm for eliminating nonlinear error based on global statistical phase feature function (GSPF) is proposed. The phase distribution can be estimated from the difference between the global cumulative distribution function and the normalized cumulative distribution function. For an ideal fringe pattern without nonlinear error and a fringe pattern with nonlinear error, the region wrapped by the x-axis normalized cumulative distribution function is much smaller than the region wrapped by the x-axis global cumulative distribution function, and the larger the nonlinear error is, the larger the difference between the two is. Therefore, the global statistical phase feature function can be used for nonlinear error correction. Then the optimal nonlinear error correction is performed based on the minimum difference between the compensated phase entropy and the ideal phase entropy. The method does not require too many steps of phase-shifting, and only three fringe patterns are needed to realize accurate and robust correction. Experimental results show that the method is fast, highly accurate and robust. Using this technique, high accuracy measurements can be achieved with the traditional three-step phase-shifting algorithm.

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Section: Introductionmentioning

confidence: 99%

The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function

Zhu,

Xu,

Long

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The double three-step method [13] suppresses the error through the internal law. In addition, the double five-step [14], double N-step [15] and three-step with three-frequency [16] methods are also proposed. However, such methods need to project a large number of images, which increases the time cost of a single measurement.…”

Section: Introductionmentioning

confidence: 99%

Point-Wise Phase Estimation Method in Fringe Projection Profilometry under Non-Sinusoidal Distortion

Yin

Liu

Zhang

et al. 2022

Sensors

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In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting.

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“…Algorithms for obtaining depth information (or unfolding phase) from fringe images usually require two main steps: phase extraction represented by phase shifting and Fourier transform methods [4] and phase unfolding represented by spatial phase unwrapping and time phase unwrapping [5,6]. With the successful application of deep learning in the field of 3D measurement, 3D reconstruction technology [7][8][9] based on Convolutional Neural Network (CNN) has been continuously developed.…”

mentioning

confidence: 99%

Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

Zhang¹,

Cao²

2022

Computers, Materials &Amp; Continua

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The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved target frame regression loss, which effectively improves the accuracy and real-time performance of feature detection. Simulation experiment results show that the model size after the improved algorithm is only 52 MB, the mean average accuracy (mAP) of fringe image data reconstruction reaches 82.11%, and the 3D point cloud restoration rate reaches 90.1%. Compared with the existing model, it has obvious advantages and can satisfy the accuracy and real-time requirements of reconstruction tasks in resource-constrained equipment.

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Phase Error Analysis and Correction for Crossed-Grating Phase-Shifting Profilometry

Cited by 7 publications

References 37 publications

The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function

The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function

Point-Wise Phase Estimation Method in Fringe Projection Profilometry under Non-Sinusoidal Distortion

Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

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