A Three-Dimensional Measurement Method Based on Multiple Reflected Images

Shunyu, 徐顺雨 Xu; Xiao, 徐晓 Xu

doi:10.3788/aos202040.1612001

Cited by 3 publications

(3 citation statements)

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“…Our research team proposed a line structured light 3D reconstruction method based on multiple reflection imaging mechanism [6], which attempts to enhance the noise-resistant performance by integrating the information of multiple simultaneously imaged laser stripes. However, the different signal-to-noise ratios of the laser stripes in one image of multiple reflection imaging are still challenging to centerline extraction algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…4.5. 3D reconstruction resultTo verify the effectiveness of the proposed method, we use both three centerline extraction methods in the process of multiple reflection imaging 3D reconstruction[6] of a metal object shown in figure 11. We adopt five laser stripes from left to right in each image, where the first EECT-2023 Journal of Physics: Conference Series 2522 (2023) 012015…”

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confidence: 99%

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A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

Huang

2023

J. Phys.: Conf. Ser.

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To overcome the stray light noise in the centerline extraction method during line structured light 3D reconstruction process, an end-to-end trainable neural network for laser stripe centerline extraction based on Convolutional Neural Network and Multi-Layer Perception is proposed. The proposed network can self-adapt to a variety of lighting (brightness) conditions and overcome the interference of different stray lights. In addition, unlike prior work on enhancing the accuracy of centerline extraction using deep learning methods that only performs it for noise reduction in pre-processing, the proposed network unifies the noise reduction and prediction processes, so that it can be optimized end-to-end directly on centerline extraction performance. The network learns an intermediate feature representation of noise reduction, which requires less complexity for data annotation, reduces the training difficulty, and has more scalability. Experiments show that the proposed method can perform centerline extraction with relatively high accuracy for laser stripes of different widths, brightness, and inclination, thus obtaining a smooth and stable reconstructed surface in the structured light 3D reconstruction process.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

Huang

2023

J. Phys.: Conf. Ser.

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“…12 ~N II  . We substitute them respectively into(2) [15~17]to obtain the wrapped phase and save as wrapped no shift  − and wrapped shift  , where, I is the collected fringe image and N is the number of one group phase shift fringe images.…”

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confidence: 99%

A Complementary Gray Code Like Double N-Step Phase Shift Method Without Gray Code Fringe Images

Han,

Yang,

et al. 2023

IEEE Photonics J.

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The complementary Gray code double N-step phase shift method is widely used because of its high detection accuracy and good robustness. However, phase unwrapping methods have the problem of low detection efficiency. In order to solve above problem, this paper proposes a complementary Gray code like double N-step phase shift method without Gray code fringe images. Firstly, the wrapped phases of the different groups of phase shift fringe images are obtained, then we get the phase difference and the fused wrapped phase, and the phase difference is converted into Gray code series. Finally, the unwrapped phase of the measured target is obtained by combining the fused wrapped phase and the Gray code series. This method in this paper improves the detection efficiency. To verify the effectiveness of method proposed in this paper, the method is compared with the complementary Gray code double N-step phase shift method, the complementary Gray code 2N-step phase shift method and the phase derivative variance method. The experimental results show that the method in this paper could effectively obtain the highprecision unwrapped phase, and the efficiency is 33.3% higher than the complementary Gray code double N-step phase shift method and the complementary Gray code 2N-step phase shift method. Index Terms-Double N-step Phase Shift Method; Phase difference; Complementary Gray code Like; fused wrapped phase; Phase unwrapping I. INTRODUCTIONn the three-dimensional field of high-speed measurement, grating projection profilometry is gradually applied to various fields because of its advantages of high precision, non-contact and low cost [1~4]. For example: aerospace field, medical field, cultural relic protection field, etc. Phase unwrapping is an important part of grating projection profilometry. When measured target contour, the arctangent function is used to solve the phase in the phase calculation process. Since the obtained result is the wrapped phase distributed in ( , ]  −[5~8], it is necessary to use the phase unwrapping algorithm to recover the wrapped phase into unwrapped phase. The advantages of complementary Gray code phase unwrapping method are high detection accuracy and robustness, however, it needs to add Gray code images to participate in the detection, which would reduce the detection

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High dynamic range fringe pattern acquisition based on deep neural network

Yang

Zhou

et al. 2022

Optics Communications

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A Three-Dimensional Measurement Method Based on Multiple Reflected Images

Cited by 3 publications

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A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

A Complementary Gray Code Like Double N-Step Phase Shift Method Without Gray Code Fringe Images

High dynamic range fringe pattern acquisition based on deep neural network

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