Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection

Wang, Jinglei; Li, Yixuan; Ji, Yifan; Qian, Jiaming; Che, Yuxuan; Zuo, Chao; Chen, Qian; Feng, Shijie

doi:10.3390/s22176469

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…In the last decade, with the application of deep learning in optical metrology [31], such as image denoising [32,33], fringe pattern enhancement [34], wrapped phase retrieval [35], phase unwrapping [36], and stereo matching [37], many researchers have applied deep learning to FPP in order to obtain accurate wrapped phases with fewer patterns and further achieve phase unwrapping. Qian et al [38] proposed a single-shot absolute 3D shape measurement with deeplearning-based color FPP.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the SPPWM pattern is significantly affected by higher harmonics and speckle patterns. In the case where traditional algorithms cannot simultaneously eliminate the interference of high-order harmonics and speckle patterns, based on the potential of deep learning in eliminating high-order harmonics [45] and noise [32,46], we proposed a sinusoidal pattern reconstruction network (SPRNet) that can simultaneously eliminate high-order harmonics and speckle patterns in SPPWM patterns to obtain high-quality sinusoidal patterns. In addition, considering the distribution characteristics of speckle patterns in SPPWM pattern, we proposed a multi-temporal spatial correlation matching algorithm (MTSCMA), which achieves a more reliable coarse disparity map calculation.…”

Section: Introductionmentioning

confidence: 99%

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Three 1-bit speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Zheng,

Li,

Zuo

et al. 2024

Meas. Sci. Technol.

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In 3D shape measurement techniques using structured light, 1-bit pulse-width modulation (PWM) patterns and 1-bit speckle patterns can be projected at high speed. However, when combining PWM and speckle patterns to integrate their advantages, the decoupling problem is insurmountable. In this work, a novel 1-bit speckle-embedded pulse-width-modulation (SP-PWM) patterns was proposed to achieve absolute 3D shape measurement using only three binary patterns. Our method consists of three main steps: First, a sinusoidal pattern reconstruction network (SPRNet) was proposed to eliminate the high-order harmonics and speckle patterns in the SP-PWM patterns and obtain high-quality sinusoidal patterns. Second, a multi-temporal spatial correlation matching algorithm (MTSCMA) was proposed to obtain a coarse disparity map from the three SP-PWM patterns. Third, the high-accuracy wrapped phase map is used as an additional constraint for refining the coarse disparity map to obtain the final high-accuracy disparity map for absolute 3D measurement without phase unwrapping. Our method combines the advantages of fringe projection profilometry techniques for high-precision wrapped phase retrieval and speckle correlation matching algorithms for robust and unambiguous disparity map calculation. The experimental results demonstrated that our method could realize high-precision absolute 3D shape measurement with an accuracy of 0.057mm using only three 1-bit SP-PWM patterns. Furthermore, different simulation noises were used to demonstrate the robustness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three 1-bit speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Zheng,

Li,

Zuo

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The latter fringe-to-phase group aims to transform fringe pattern(s) into several potential intermediate outputs before determining the unwrapped phase and 3D shape by the conventional technique [47][48][49][50]. Here, unwrapped refers to the demodulation of the wrapped phase because conventional algorithms generally yield phase data wrapped in a small value range.…”

Section: Introductionmentioning

confidence: 99%

Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning

Nguyen

Lam

et al. 2023

Sensors

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Three-dimensional (3D) shape acquisition of objects from a single-shot image has been highly demanded by numerous applications in many fields, such as medical imaging, robotic navigation, virtual reality, and product in-line inspection. This paper presents a robust 3D shape reconstruction approach integrating a structured-light technique with a deep learning-based artificial neural network. The proposed approach employs a single-input dual-output network capable of transforming a single structured-light image into two intermediate outputs of multiple phase-shifted fringe patterns and a coarse phase map, through which the unwrapped true phase distributions containing the depth information of the imaging target can be accurately determined for subsequent 3D reconstruction process. A conventional fringe projection technique is employed to prepare the ground-truth training labels, and part of its classic algorithm is adopted to preserve the accuracy of the 3D reconstruction. Numerous experiments have been conducted to assess the proposed technique, and its robustness makes it a promising and much-needed tool for scientific research and engineering applications.

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“…In other studies, the researchers [ 57 , 64 ] employed two subnetworks with cosine fringe pattern and multi-code/reference pattern to obtain the wrapped phase and fringe orders. The work reported in [ 65 , 66 ] followed a framework comprising two deep neural networks, aiming to enhance the quality of the fringe pattern and accurately determine the numerator and denominator through denoising patterns. Machineni et al [ 67 ] presented an end-to-end deep learning-based framework for 3D object profiling, and the method encompassed a two-stage process involving a synthesis network and a phase estimation network.…”

Section: Introductionmentioning

confidence: 99%

Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

Nguyen,

Wang

2023

Sensors

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In recent years, integrating structured light with deep learning has gained considerable attention in three-dimensional (3D) shape reconstruction due to its high precision and suitability for dynamic applications. While previous techniques primarily focus on processing in the spatial domain, this paper proposes a novel time-distributed approach for temporal structured-light 3D shape reconstruction using deep learning. The proposed approach utilizes an autoencoder network and time-distributed wrapper to convert multiple temporal fringe patterns into their corresponding numerators and denominators of the arctangent functions. Fringe projection profilometry (FPP), a well-known temporal structured-light technique, is employed to prepare high-quality ground truth and depict the 3D reconstruction process. Our experimental findings show that the time-distributed 3D reconstruction technique achieves comparable outcomes with the dual-frequency dataset (p = 0.014) and higher accuracy than the triple-frequency dataset (p = 1.029 × 10−9), according to non-parametric statistical tests. Moreover, the proposed approach’s straightforward implementation of a single training network for multiple converters makes it more practical for scientific research and industrial applications.

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Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection

Cited by 8 publications

References 33 publications

Three 1-bit speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Three 1-bit speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning

Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

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