How do phase-shifting algorithms work?

Juarez-Salazar, Rigoberto; Mendoza-Rodriguez, Ceciibet; Hernandez-Beltran, Jose Enrique; Robledo-Sánchez, Carlos

doi:10.1088/1361-6404/aae3c2

Cited by 15 publications

(5 citation statements)

References 42 publications

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“…9 Once the camera parameters are obtained, the triangulation method is applied to determine the 3D points. [10][11][12][13] However, the estimated points contain noisy measurements. Thus, the bundle adjustment can be applied to minimize the re-projection error of the 3D points for all estimated poses.…”

Section: Epipolar Geometrymentioning

confidence: 99%

Vision-based pose estimation for robot navigation in an uncontrolled environment

Zheng Wu,

Juarez-Salazar,

Diaz-Ramirez

2023

Optics and Photonics for Information Processing XVII

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Location and pose estimation are essential tasks for robot navigation. Conventional global positioning systems can perform poorly due to environmental or indoor interference. Alternatively, vision-based location and pose estimation systems may be more suitable for indoor and outdoor applications. However, vision-based systems still need to improve their robustness in an uncontrolled environment and operational performance. In this work, a visual pose estimation method for robot navigation in an uncontrolled environment is proposed. The theoretical principles of pose estimation are reviewed and the usefulness of the proposed approach in a navigation sequence is shown. The results obtained show that the proposed method is feasible for robot navigation applications.

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Section: Epipolar Geometrymentioning

confidence: 99%

Vision-based pose estimation for robot navigation in an uncontrolled environment

Zheng Wu,

Juarez-Salazar,

Diaz-Ramirez

2023

Optics and Photonics for Information Processing XVII

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“…Unfortunately, the demodulated phase accuracy may be significantly lower since the background and modulation intensity of the three grayscale fringe images separated from one color fringe image stand not consistent [29,30]. This is an inherent problem of the measurement system, which is well known as color coupling and imbalance [27,30], which unavoidable factor that invalidates the advantages of conventional PSP [31,32].…”

Section: Introductionmentioning

confidence: 99%

A fast three-dimensional measurement method based on color fringe projection and background intensity calibration

Zhu,

Du,

Tang

et al. 2024

Preprint

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Phase-shifting profilometry (PSP) is one of the most widely available methods in three-dimensional (3D) measurement. However, PSP is not suitable for dynamic measurements due to multiple projections. The color fringe projection profilometry(CFPP) can encode three grayscale fringe patterns in a single color pattern, preserving the robustness of PSP while enabling dynamic measurements. But for the CFPP measurement system, color coupling and imbalance phenomena are inevitable between the RGB channels, which increases the measurement error. This paper proposes a background intensity calibration (BIC) method to correct the color imbalance and reconstruct 3D shapes by a single-shot projected-captured. First, capture a color fringe image and extract the background intensity by searching the upper and lower envelopes to get three background-free fringe images; Secondly, solve the normalized coefficient to balance the three background-free images. As a result, a color fringe image can be split into three phase-shifting fringe images with the normalized modulation intensity and good sinusoidal aiming to perform phase demodulation by three-step PSP; Thirdly, the demodulation phase is unwrapped using a simple solving fringe orders algorithm based on a positive and negative binarization (PNB) method. Several experiments on dynamic scenes were performed, verifying that the measurement speed and accuracy of the proposed BIC method are not limited by the object moving, which can achieve fast, dense, and accurate 3D measurements.

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“…In the easiest way the phase function can be retrieved by creating the system of equations and finding the solution for phase map 'unknown' φ (x, y). This is the working principle of the algorithm called temporal phase shifting (TPS) [12][13][14][15][16][17][18][19], where the system of interferometric equations is defined by adding the phase shift (known [13][14][15] or unknown [16][17][18][19]) between the two coherent beams. In general, multi-frame approach has limited time resolution since multiple frames need to be collected and therefore the analysis of fast dynamic events or in an unstable environment is troublesome.…”

Section: Introductionmentioning

confidence: 99%

DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtration

Cywińska

Szumigaj

Kołodziej

et al. 2023

J. Opt.

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The algorithms defined as variational image decomposition (VID) constitute the state-of-the-art in solving the image prefiltration problem. However, the discussion about the advantages and disadvantages of different variational image decomposition models in the context of fringe pattern prefiltration is yet to be addressed and this work is the first one leaning into this issue. The unsupervised variational image decomposition (uVID) algorithm allows for automatic, accurate and robust preprocessing of diverse fringe patterns and introduces the parameters and stopping criterion for Chambolle’s iterative projection algorithm to separate the fringes and background. However, determining the stopping criterion in each iteration is a severely time-consuming process, which is particularly important given the fact that in many cases thousands of iterations must be calculated to obtain a satisfactory fringe pattern decomposition result. Therefore, the idea of using convolutional neural network to map the relationship between the fringe pattern spatial intensity distribution and the required number of Chambolle projection iterations has emerged. That way, it is no longer required to determine the value of the stopping criterion in every iteration, but the appropriate number of iterations is known in advance via machine learning process. We showed that the calculation time is reduced on average by 3-4 times by employing the deep learning-based acceleration (DeepVID) without jeopardizing the overall accuracy of the prefiltration. This way an important progress in developing uVID algorithm features towards real-time studies of dynamic phenomena is reported in this contribution. For the sake of metrological figure of merit, we employ deep learning based solution, for the first time to the best of our knowledge, to accelerate powerful and well-established VID approach, not to bypass it completely.

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How do phase-shifting algorithms work?

Cited by 15 publications

References 42 publications

Vision-based pose estimation for robot navigation in an uncontrolled environment

Vision-based pose estimation for robot navigation in an uncontrolled environment

A fast three-dimensional measurement method based on color fringe projection and background intensity calibration

DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtration

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