Real-time 3D shape measurement using 3LCD projection and deep machine learning

Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches

The field of computer vision has been focusing on achieving accurate three-dimensional (3D) object representations from a single two-dimensional (2D) image through deep artificial neural networks. Recent advancements in 3D shape reconstruction techniques that combine structured light and deep learning show promise in acquiring high-quality geometric information about object surfaces. This paper introduces a new single-shot 3D shape reconstruction method that uses a nonlinear fringe transformation approach through both supervised and unsupervised learning networks. In this method, a deep learning network learns to convert a grayscale fringe input into multiple phase-shifted fringe outputs with different frequencies, which act as an intermediate result for the subsequent 3D reconstruction process using the structured-light fringe projection profilometry technique. Experiments have been conducted to validate the practicality and robustness of the proposed technique. The experimental results demonstrate that the unsupervised learning approach using a deep convolutional generative adversarial network (DCGAN) is superior to the supervised learning approach using UNet in image-to-image generation. The proposed technique's ability to accurately reconstruct 3D shapes of objects using only a single fringe image opens up vast opportunities for its application across diverse real-world scenarios.

SE-FSCNet: full-scale connection network for single-shot phase demodulation

The accuracy of phase demodulation has significant impact on the accuracy of fringe projection 3D measurement. Currently, researches based on deep learning methods for extracting wrapped phase mostly use U-Net as the subject of network. The connection method between its hierarchies has certain shortcomings in global information transmission, which hinders the improvement of wrapped phase prediction accuracy. We propose a single-shot phase demodulation method for fringe projection based on a novel full-scale connection network SE-FSCNet. The encoder and decoder of the SE-FSCNet have the same number of hierarchies but are not completely symmetrical. At the decoder a full-scale connection method and feature fusion module are designed so that SE-FSCNet has better abilities of feature transmission and utilization compared with U-Net. A channel attention module based on squeeze and excitation is also introduced to assign appropriate weights to features with different scales, which has been proved by the ablation study. The experiments conducted on the test set have demonstrated that the SE-FSCNet can achieve higher precision than the traditional Fourier transform method and the U-Net in phase demodulation.

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

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.

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

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.

Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning

Single-shot 3D shape reconstruction integrating structured light and deep learning has drawn considerable attention and achieved significant progress in recent years due to its wide-ranging applications in various fields. The prevailing deep-learning-based 3D reconstruction using structured light generally transforms a single fringe pattern to its corresponding depth map by an end-to-end artificial neural network. At present, it remains unclear which kind of structured-light patterns should be employed to obtain the best accuracy performance. To answer this fundamental and much-asked question, we conduct an experimental investigation of six representative structured-light patterns adopted for single-shot 2D-to-3D image conversion. The assessment results provide a valuable guideline for structured-light pattern selection in practice.

DeepOrientation: convolutional neural network for fringe pattern orientation map estimation

Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process in various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation, and fringe pattern phase demodulation. Considering all of that, the accurate, robust, and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose a novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with a representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper, is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy.

Efficient freeform-based pattern projection system for 3D measurements

For three-dimensional (3D) measurement of object surface and shape by pattern projection systems, we used a hybrid projection system, i.e., a combination of a projection lens and a transmissive freeform to generate an aperiodic sinusoidal fringe pattern. Such a freeform effects a light redistribution, thus leading to an effective and low-loss pattern projection, as it increases the total transmission intensity of the system and has less power dissipation than classical projection systems. In this paper, we present the conception and realization of the measurement setup of a transmissive fringe projection system. We compare the characteristics of the generated intensity distribution with the classical system based on GOBO (GOes Before Optics) projection and show measurement results of different surface shapes, recorded with the new system.

Deep learning in optical metrology: a review

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.

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement

Single-shot fringe projection profilometry (FPP) is essential for retrieving the absolute depth information of the objects in high-speed dynamic scenes. High-precision 3D reconstruction using only one single pattern has become the ultimate goal in FPP. The frequency-multiplexing (FM) method is a promising strategy for realizing single-shot absolute 3D measurement by compounding multi-frequency fringe information for phase unwrapping. In order to solve the problem of serious spectrum aliasing caused by multiplexing schemes that cannot be removed by traditional spectrum analysis algorithms, we apply deep learning to frequency multiplexing composite fringe projection and propose a composite fringe projection deep learning profilometry (CDLP). By combining physical model and data-driven approaches, we demonstrate that the model generated by training an improved deep convolutional neural network can directly perform high-precision and unambiguous phase retrieval on a single-shot spatial frequency multiplexing composite fringe image. Experiments on both static and dynamic scenes demonstrate that our method can retrieve robust and unambiguous phases information while avoiding spectrum aliasing and reconstruct high-quality absolute 3D surfaces of objects only by projecting a single composite fringe image.

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

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.

MIMONet: Structured-light 3D shape reconstruction by a multi-input multi-output network

Reconstructing 3D geometric representation of objects with deep learning frameworks has recently gained a great deal of interest in numerous fields. The existing deep-learning-based 3D shape reconstruction techniques generally use a single red-green-blue (RGB) image, and the depth reconstruction accuracy is often highly limited due to a variety of reasons. We present a 3D shape reconstruction technique with an accuracy enhancement strategy by integrating the structured-light scheme with deep convolutional neural networks (CNNs). The key idea is to transform multiple (typically two) grayscale images consisting of fringe and/or speckle patterns into a 3D depth map using an end-to-end artificial neural network. Distinct from the existing autoencoder-based networks, the proposed technique reconstructs the 3D shape of target using a refinement approach that fuses multiple feature maps to obtain multiple outputs with an accuracy-enhanced final output. A few experiments have been conducted to verify the robustness and capabilities of the proposed technique. The findings suggest that the proposed network approach can be a promising 3D reconstruction technique for future academic research and industrial applications.

Convolutional neural network for estimating physical parameters from Newton's rings

By analyzing Newton's rings, often encountered in interferometry, the parameters of spherical surfaces such as the rings' center and the curvature radius can be estimated. First, the classical convolutional neural networks, visual geometry group (VGG) network and U-Net, are applied to parameter estimation of Newton's rings. After these models are trained, the rings' center and curvature radius can be obtained simultaneously. Compared with previous analysis methods of Newton's rings, it is shown that the proposed method has higher precision, better immunity to noise, and lower time consumption. For a Newton's rings pattern of ${{640}} \times {{480}}$ pixels comprising ${-}{{5}}\;{\rm{dB}}$ Gaussian noise or 60% salt-and-pepper noise, the parameters can be estimated by the VGG model in 0.01 s, the error of the rings' center is less than one pixel, and the error of curvature radius is lower than 0.5%.

Accuracy improvement of time-of-flight depth measurement by combination of a high-resolution color camera

Time-of-flight (ToF) cameras can acquire the distance between the sensor and objects with high frame rates, offering bright prospects for ToF cameras in many applications. Low-resolution and depth errors limit the accuracy of ToF cameras, however. In this paper, we present a flexible accuracy improvement method for depth compensation and feature points position correction of ToF cameras. First, a distance-error model of each pixel in the depth image is established to model sinusoidal waves of ToF cameras and compensate for the measured depth data. Second, a more accurate feature point position is estimated with the aid of a high-resolution camera. Experiments evaluate the proposed method, and the result shows the root mean square error is reduced from 4.38 mm to 3.57 mm.

Deep learning-based fringe modulation-enhancing method for accurate fringe projection profilometry

Fringe projection profilometry (i.e., FPP) has been one of the most popular 3-D measurement techniques. The phase error due to system random noise becomes non-ignorable when fringes captured by a camera have a low fringe modulation, which are inevitable for objects' surface with un-uniform reflectivity. The phase calculated from these low-modulation fringes may have a non-ignorable phase error and generate 3-D measurement error. Traditional methods reduce the phase error with losing details of 3-D shapes or sacrificing the measurement speed. In this paper, a deep learning-based fringe modulation-enhancing method (i.e., FMEM) is proposed, that transforms two low-modulation fringes with different phase shifts into a set of three phase-shifted high-modulation fringes. FMEM enables to calculate the desired phase from the transformed set of high-modulation fringes, and result in accurate 3-D FPP without sacrificing the speed. Experimental analysis verifies its effectiveness and accurateness.

Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.