Color full stokes polarization fringe projection 3D imaging

Liang, Jian; Tian, Xiaobo; Tu, Xingzhou; Spires, Oliver; Brock, Neal; Wang, Daodang; Wu, Heng; Ren, Liyong; Pau, Stanley; Liang, Rongguang

doi:10.1016/j.optlaseng.2020.106088

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Those disconnected labels will cause the counting error in network II; therefore, before sending the outputs to network II, a detection algorithm of the disconnected labels is added to eliminate this error by reconnecting the disconnected labels, and this can be easily realized by morphological operation carried in OpenCV module in Python. The integer map from network II is shown in Figure 8 c and the unwrapped phase is shown in Figure 8 d. By subtracting the unwrapped phase from the background, the phase of the tooth model is plotted in Figure 8 e [ 20 ]. Figure 8 f is the color 3D reconstruction image of the tooth model combined with color information.…”

Section: Methodsmentioning

confidence: 99%

“…With the help of Figure 13 a, we can transform the phase feature map obtained from the phase shift fringe with high frequency (as shown in Figure 13 b) to the phase feature map with appropriately assigned integer to each segment (as shown in Figure 13 c). Using Equation (1), the unwrapped phase can be finally obtained, as shown in Figure 13 d. Based on Figure 13 d, one can reconstruct the 3D surfaces of the three paper cups by subtracting the phase of the background [ 20 ], and clearly see the depth of each paper cup. Note that, here we only show the relative position of the three paper cups.…”

Section: Methodsmentioning

confidence: 99%

“…The integer map from network II is shown in Figure 8c and the unwrapped phase is shown in Figure 8d. By subtracting the unwrapped phase from the background, the phase of the tooth model is plotted in Figure 8e [20]. Figure 8f is the color 3D reconstruction image of the tooth model combined with color information.…”

Section: Methodsmentioning

confidence: 99%

“…Using Equation (1), the unwrapped phase can be finally obtained, as shown in Figure 13d. Based on Figure 13d, one can reconstruct the 3D surfaces of the three paper cups by subtracting the phase of the background [20], and clearly see the depth of each paper cup. Note that, here we only show the relative position of the three paper cups.…”

Section: Sensors 2020 20 X For Peer Review 8 Of 11mentioning

confidence: 99%

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Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Liang

Zhang

Shao

et al. 2020

Sensors

Self Cite

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Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Sensors 2020 20 X For Peer Review 8 Of 11mentioning

confidence: 99%

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Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Liang

Zhang

Shao

et al. 2020

Sensors

Self Cite

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“…Liang et al developed a measurement system that can obtain surface shape, color, and polarization information of objects in one shot. 21 This method can lay the foundation for potential high-speed measurements. However, the use of customized equipment has led to increased costs.…”

Section: Introductionmentioning

confidence: 99%

Robust 3D reconstruction technology based on ternary polarization coding strategy

Zhu,

Zhou,

Xie

et al. 2023

Opt. Eng.

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Fringe projection profilometry (FPP) has been widely used in various fields because of its high precision and efficiency. However, fringe images containing overexposed areas are obtained by traditional FPP for high-reflective object surfaces, which leads to errors in absolute phase and three-dimensional (3D) reconstruction results. A robust 3D reconstruction technique based on a polarization state-based ternary polarization coding strategy is proposed to address these errors. Instead of the light intensity coding patterns, the polarization coding patterns are used in this method. Next, the S 1 image is calculated using the polarization image captured by the camera. The Stokes parameter is utilized to segment the fringe codewords without using a specially designed threshold. The proposed method enhanced the stability of the fringes, improved the quality of the fringes, and reduced the number of fringe patterns. Experimental results verified that the proposed method has higher robustness than the conventional method in the measurement of objects with high-reflective surfaces. The results reveal that the proposed method can suppress the effect of highlight areas in the reconstruction of high-reflective surfaces to obtain accurate absolute phases and reconstruct the 3D point cloud data reliably.

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Accurate three-dimensional measurement based on polarization-defocused encoded structured light

et al. 2022

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Color full stokes polarization fringe projection 3D imaging

Cited by 10 publications

References 22 publications

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Robust 3D reconstruction technology based on ternary polarization coding strategy

Accurate three-dimensional measurement based on polarization-defocused encoded structured light

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