Has 3D finally come of age? ——An introduction to 3D structured-light sensor

Zuo, Chao; Zhang, Xiaolei; Hu, Yan; Yin, Wei; Detong, Shen; Zhong, Jianlan; Jing, Zheng; Chen, Qian

doi:10.3788/irla202049.0303001

Cited by 30 publications

(10 citation statements)

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“…The increasing demand for 3D measurement technology across various fields has driven the development of digital technology. Among the available techniques, fringe projection profilometry (FPP) stands out due to its non-contact nature, high reconstruction accuracy, and simple system structure 1,2,3 . However, FPP encounters challenges when measuring highly reflective regions of objects, leading to overexposure.…”

Section: Introductionmentioning

confidence: 99%

3D imaging of pathological samples based on adaptive fringe luminance projection

Wei,

Hu,

Ding

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The imaging saturation can lead to errors in image processing and result in incomplete or inaccurate three-dimensional (3D) outcomes. To address this problem, we propose an adaptive fringe luminance projection method based on iterative luminance adjustments to the pattern regions corresponding to the highlight targets. After 3-4 times iterations to update the intensity distribution of the patterns, we can ensure that the captured images are without overexposed area; thus, the intensity variations provide valid information for the subsequent 3D reconstruction. The experimental results show the accurate and complete 3D reconstruction for pathological sample surfaces, which reaches the requirements for 3D digitalization of pathological samples.

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Section: Introductionmentioning

confidence: 99%

3D imaging of pathological samples based on adaptive fringe luminance projection

Wei,

Hu,

Ding

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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“…With the rapid development of wearable devices, augmented reality (AR) [1,2], and the Internet of Things, there is a growing need for smaller, lighter, and cheaper imaging systems [3]. Lensless imaging is an emerging imaging method that employs optical elements instead of lenses and uses computational models to reconstruct images from highly multiplexed measurements [4][5].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of lensless images using multi-scale attention block

Qinghan,

yourong,

zhengcao

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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“…In optical metrology, based on physical models of the image formation, the observed measurements can be transformed into the desired physical properties of the objects. For many optical measurement techniques such as interferometry, digital holography, and fringe projection profilometry (FPP), [2][3][4][5] the accuracy and efficiency of phase retrieval from the recorded fringe images are essential to reconstruct various underlying quantities dynamically. The most efficient method for phase measurment is recovering the phase distribution from a single fringe image, but as a typical case in optical metrology, it is an ill-posed inverse problem.…”

Section: Introductionmentioning

confidence: 99%

A lightweight fringe analysis network based on deep learning

Che,

Yin

2023

AOPC 2023: Computing Imaging Technology

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Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks, such as fringe denoising, phase unwrapping, and fringe analysis. However, the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naïve, as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice. To this end, we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (LeFTP) module. By parameterizing conventional phase retrieval methods, the LeFTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples, while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods. Guided by the initial phase from LeFTP, the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks. Experimental results demonstrate PI-FPA enables more accurate and computationally efficient single-shot phase retrieval.

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Has 3D finally come of age? ——An introduction to 3D structured-light sensor

Cited by 30 publications

References 0 publications

3D imaging of pathological samples based on adaptive fringe luminance projection

3D imaging of pathological samples based on adaptive fringe luminance projection

Reconstruction of lensless images using multi-scale attention block

A lightweight fringe analysis network based on deep learning

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