Deep neural networks for single shot structured light profilometry

Jeught, Sam Van der; Dirckx, Joris J.J.

doi:10.1364/oe.27.017091

Cited by 115 publications

(42 citation statements)

References 12 publications

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“…b , c The input and output of Nguyen’s method 381 . d , e The input and output of Van’s method 382 . f , g The input and output of Machineni’s method 384 .…”

Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

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“…b , c The input and output of Nguyen’s method 381 . d , e The input and output of Van’s method 382 . f , g The input and output of Machineni’s method 384 .…”

Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The metrics include root-mean-square error (RMSE), mean error, median error, trimean error, mean of the best 25%, and mean of the worst 25%. Because using an endto-end neural network to directly transform a single-shot fringe or speckle image into its corresponding 3D shape or depth map has most recently gained a great deal of interest [46,47], a comparison with such a fringe-to-depth network and a speckle-to-depth network is conducted. Moreover, a fringe-to-ND network [32,34,43,48] is carried out for comparison as well since it is an approach falling between the fringe-to-depth and the proposed fringe-to-fringe network.…”

Section: Resultsmentioning

confidence: 99%

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Nguyen

Wang

2021

Photonics

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Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.

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“…Now, there are many works on end-to-end height map directly. In [19], a network with 10 convolutional layers is built for full-field height extraction from structured-light pattern. [20] utilizes depth-wise separable convolution to build a deep neural network, which can reduce the number of learnable parameters of the model, and the accuracy of 3D reconstruction does not decrease.…”

Section: Related Workmentioning

confidence: 99%

3D Reconstruction From Structured-Light Profilometry With Dual-Path Hybrid Network

Wang

Qiu

et al. 2022

Preprint

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With the rapid development of high-speed image sensors and optical imaging technology, these have effectively promoted the improvement of non-contact 3D shape measurement. Among them, striped structured-light technology has been widely used because of its high measurement accuracy. Compared with classical methods such as Fourier transform profilometry, many deep neural networks are utilized to restore 3D shape from single shot structured-light. In actual engineering deployments, the number of learnable parameters of convolution neural network (CNN) is huge, especially for high-resolution structured-light patterns. To this end, we proposed a dual-path hybrid network based on UNet, which eliminates the deepest convolution layers to reduce the number of learnable parameters, and a swin transformer path is additionally built on the decoder to improve the global perception of this network. The experimental results show that the learnable parameters of the model are reduced by 60% compared with the UNet, and the measurement accuracy is not degraded at the same time. The proposed dual-path hybrid network provides an effective solution for structured-light 3D reconstruction and its practice in engineering.

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Deep neural networks for single shot structured light profilometry

Cited by 115 publications

References 12 publications

Deep learning in optical metrology: a review

Deep learning in optical metrology: a review

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

3D Reconstruction From Structured-Light Profilometry With Dual-Path Hybrid Network

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