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The three-dimensional(3D) measurement technology based on structured light fringe projection has the advantages of high efficiency, simplicity and non-contact acquisition. However, its measurement quality is heavily dependent on the reflective properties of the target surface. For a highly reflective surface, overexposure may be caused in some areas of the image, bringing decoding errors and causing the degradation of accuracy or even measurement failure. In recent years, a lot of researches have been done on this problem and many methods have been proposed. However, a systematic survey of these methods is still lacking. In this work, a review of the structured light 3D measurement techniques for highly reflective surfaces is presented. Firstly, the challenges posed by surface reflectivity to structured light 3D measurement are analyzed. Secondly, recent methods, which are classified into two categories, are reviewed. Finally, the accuracy and efficiency of these methods are discussed, and the trends of future researches are summarized. [ABSTRACT FROM AUTHOR]

The structured light measurement method is applied in quality control across various sectors including manufacturing, medicine, fashion, and technology. This approach facilitates the generation of rapid and consistent evaluation reports by analyzing errors, geometric dimensions, and deviations in measured parts, aligning them with design specifications. Such capabilities significantly enhance the speed and quality of production processes, particularly in the mechanical industry. This paper proposes an effective alternative solution for the inaccurate measurement of shiny mechanical surfaces. It explores ambient light to achieve optimal calibration and enhance measurement accuracy. The appropriate exposure time for surface measurement was determined by analyzing the image intensity histogram. This step helps avoid saturation in the detection device and ensures the generation of high-quality point clouds by merging them at suitable exposure times. To validate the effectiveness of the proposed system, two comparisons were conducted: one between the 3D profile of the part measured by commercial software versus the proposed method, and another between the 3D profile of the plastic part as measured by the proposed method versus its designed profile. The results demonstrate the efficiency of the proposed method in accurately testing full-field reflective surfaces, without requiring additional equipment or hardware.

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. [ABSTRACT FROM AUTHOR]

The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3–4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades. [ABSTRACT FROM AUTHOR]

Full-field three-dimensional (3D) measurement technology based on phase information has become an indispensable part of geometric dimension measurement in modern scientific research and engineering applications. This field has been developing and evolving for the study of highly reflective phenomena, diffuse reflections, and specular surfaces, and many novel methods have emerged to increase the speed of measurements, enhance data accuracy, and broaden the robustness of the system. Herein, we will discuss the latest research progress in full-field 3D shape measurement based on phase information systematically and comprehensively. First, the fundamentals of 3D shape measurement based on phase information are introduced, namely, phase-shifting and transform-based methods. Second, recent technological innovations are highlighted, including increases in measurement speed and automation and improvements in robustness in complex environments. In particular, the challenges faced by these technological advances in solving highly dynamic, composite surface measurement problems are presented, i.e., with multiexposure techniques proposed for high dynamics that extend the dynamic range of the camera to reduce the effects of overexposure but increase the cost of time and have high hardware requirements, fringe adaptive techniques that overcome light variations but are computationally complex, and multipolarized camera techniques that reduce the effects of light variations but are sensitive to the light source. Third, the phase-shifting method combined with coding is proposed to improve the measurement speed, but the accuracy is slightly reduced. Deep learning techniques are proposed to cope with measurements in complex environments, but the dataset computation process is cumbersome. Finally, future research directions are suggested, and the challenges are presented. Overall, this work provides a reference for researchers and engineers. Highlights: This paper reviews advancements in technology aimed at the high precision and high speed of measurements, ensuring more efficient and reliable full-field 3D shape measurements. The challenges related to the advancement of error compensation for phase calculations are outlined and discussed in various techniques with their advantages and trade-offs. The examination of future research directions and challenges in the field of full-field 3D measurements based on phase information culminates by providing insightful perspectives. [ABSTRACT FROM AUTHOR]

Robust and accurate 3D reconstruction using a limited number of fringe patterns has posed a challenge in the field of structured light 3D imaging. Unlike traditional approaches that rely on multiple fringe patterns, using only one or two patterns makes phase recovery and unwrapping difficult. To address this issue, a recurrent classification neural network (RCNN) has been developed, transforming the phase recovery and unwrapping tasks into a unified phase classification task. First, a training dataset consisting of 1200 groups of data was collected to generate a total of 38 400 training samples, enabling the RCNN to learn the mapping between the input fringe patterns and the corresponding label maps. Then, based on the well-trained network, a label map is generated based on the input two fringe patterns using the output classification results. Finally, 3D reconstruction data could be obtained by combining the inferred label map with the vision system's parameters. A series of comprehensive experiments have been conducted to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate, in vivo, a recently developed imaging system (Aeva‐HE™), based on fringe projection methodology: (i) its correlation with expert's assessments and real age of 85 French Caucasian women and (ii) its potential, as a screening tool, in rapidly selecting prototypes of tensile cosmetic products. Materials and Methods: First, the bare faces of 85 differently aged French/Caucasian women were photographed under standard conditions and further analyzed by the Aeva‐HE™ device. An expert aesthetician graded the severity of wrinkling on a fixed scale, helped by the use of a dedicated Skin Aging Atlas. A panel of 15 judges performed the same task on full‐face standard photographs. The Aeva‐HE™ software yielded various wrinkle's parameters (density, volume, mean depth, etc.) on different facial locations, according to age‐groups. Second, seven women, balanced in age and wrinkling severity, were recruited in a separate study. These women applied at Day 1 a prototype of the tensile product and at Day 2, they applied another prototype of the tensile product. The whole faces (before and after products application) of the seven studied women were captured and analyzed by the Aeva‐HE™ system. Results: The density of wrinkles was significantly highly correlated with the aesthetician scores and, unsurprisingly, with age. Some parameters (volume, depths) of different wrinkles (glabellar, crow's feet, cheeks) were quantified, showing different absolute values and of statistically different progressions with age. The amplitudes of the rapid effects brought by the two prototypes of tensile products were clearly differentiated. Conclusion: The recently developed device Aeva‐HE™ is an efficient system for rapidly establishing a faithful and precise status of facial wrinkles, in vivo and seems like a precious tool in the rapid screening of tensile products, possibly performed on a rather limited number of subjects. [ABSTRACT FROM AUTHOR]

this study presents a method to recover the response function of a flexible latex membrane undergoing a dynamic event by volume oscillation using Double Digital Fringe Projection to measure the profile of the surface in a sequence of frames. By applying a frequency filter to the interference pattern, a moiré pattern was obtained. Optical phase retrieval from individual frames was performed using the Isotropic Quadrature Transform Algorithm. This fringe projection technique enables full-field measurements, including edges where other techniques could not perform due to shadows generated by the tilt angle. The procedure was devised as a non-contact system to retrieve the deformation function of a flexible surface, enhancing a valuable aspect of the methodology. The results demonstrate successful recovery of the membrane response function, highlighting the relevance of the method for accurate deformation measurements using a straightforward system. These outcomes provide significant value for the development of precise measurement techniques for flexible structures in engineering applications, such as soft robotics, biomedical devices, and material science. [ABSTRACT FROM AUTHOR]

Fringe projection is an important technology in three-dimensional measurement and target recognition. The measurement accuracy depends heavily on the calibration of the absolute phase and projector pixels. An easy-to-implement calibration method based on the Random Sample Consensus (RANSAC) algorithm is proposed to exterminate the phase error data and elevate the measurement accuracy in a fringe projection system. The reconstruction experiments of a double-sphere standard demonstrate that the uncertainties in radius and sphere-distance measurement are reduced to one thousandth of the measured value or even less, and the standard deviation in multiple measurements is restricted to within 50 μm. The measurement accuracy provided by the proposed RANSAC method can be improved by up to 44% compared with that provided by traditional least squared method (LSM). The proposed calibration method is easy and simple to implement, and it does not need additional hardware, but rather a calibration board. [ABSTRACT FROM AUTHOR]

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art. [ABSTRACT FROM AUTHOR]

The periodic misalignment of the wrapping phase and fringe order based on the phase-shift + coding method is known to occur easily, leading to jump errors in absolute phase recovery. This paper proposes a two-phase unwrapping method based on shifting-phase coding. This approach encodes the fringe order information and the wrapping phase into the four-step phase shift mode. It generates four composite fringe patterns to directly resolve the wrapping phase and fringe order, thus improving the measurement speed by reducing the number of fringe projections. The interleaved wrapping phases are then constructed by a sequence that changes the initial wrapping phase. The two-phase unwrapping becomes the absolute phase, so the phase error is avoided and the measurement accuracy is improved. The proposed strategy's effectiveness is proved by the comparison experiments of different methods. Similarly, the suggested method's reliability is verified by the measurement experiments of several discontinuous objects. [ABSTRACT FROM AUTHOR]

Phase measurement profilometry (PMP) is primarily employed to analyze the morphology of a functional surface with precision. Historically, one of the most complex and persistent challenges in PMP has been reducing errors stemming from inconsistent indicators at the edges of a surface. In response to this challenge, we propose an optimized error compensation methodology specifically designed to handle edge artefacts. This methodology introduces the Hilbert transform and object surface albedo as tools to detect the edges of the artefact region that need to be compensated. Moreover, we analyze the characteristics of the sinusoidal fringe waveform propagation direction and investigate the reconstruction results of the fringe vertical to the current directions to compensate for edge artefacts. The experimental results for various objects show that the optimized approach can compensate for edge artefacts by projecting in two directions and reducing the projection by half. The compensated root mean square error (RMSE) for planar objects can be reduced by over 45%. [ABSTRACT FROM AUTHOR]

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.

The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3–4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades.

This study presents a method to recover the response function of a flexible latex membrane undergoing a dynamic event by volume oscillation using Double Digital Fringe Projection to measure the profile of the surface in a sequence of frames. By applying a frequency filter to the interference pattern, a moiré pattern was obtained. Optical phase retrieval from individual frames was performed using the Isotropic Quadrature Transform Algorithm. This fringe projection technique enables full-field measurements, including edges where other techniques could not perform due to shadows generated by the tilt angle. The procedure was devised as a non-contact system to retrieve the deformation function of a flexible surface, enhancing a valuable aspect of the methodology. The results demonstrate successful recovery of the membrane response function, highlighting the relevance of the method for accurate deformation measurements using a straightforward system. These outcomes provide significant value for the development of precise measurement techniques for flexible structures in engineering applications, such as soft robotics, biomedical devices, and material science.

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Fringe projection profilometry (FPP) has been broadly applied in three-dimensional (3D) measurements, but the existing multi-shot methods, which mostly utilize phase-shifting techniques, are heavily affected by the disturbance of vibration and cannot be used in dynamic scenes. In this work, a single-shot 3D measurement method using a deep neural network named the Fringe Analysis Network (FrANet) is proposed. The FrANet is composed of a phase retrieval subnetwork, phase unwrapping subnetwork, and refinement subnetwork. The combination of multiple subnetworks can help to recover long-range information that is missing for a single U-Net. A two-stage training strategy in which the FrANet network is pre-trained using fringe pattern reprojection and fine-tuned using ground truth phase maps is designed. Such a training strategy lowers the number of ground truth phase maps in the data set, saves time during data collection, and maintains the accuracy of supervised methods in real-world setups. Experimental studies were carried out on a setup FPP system. In the test set, the mean absolute error (MAE) of the refined absolute phase maps was 0.0114 rad, and the root mean square error (RMSE) of the 3D reconstruction results was 0.67 mm. The accuracy of the proposed method in dynamic scenes was evaluated by measuring moving standard spheres. The measurement of the sphere diameter maintained a high accuracy of 84 μm at a speed of 0.759 m/s. Two-stage training only requires 8800 fringe images in data acquisition, while supervised methods require 96,000 fringe images for the same number of iterations. Ablation studies verified the effectiveness of two training stages and three subnetworks. The proposed method achieved accurate single-shot 3D measurements comparable to those obtained using supervised methods and has a high data efficiency. This enables the accurate 3D shape measurement of moving or vibrating objects in industrial manufacturing and allows for further exploration of network architecture and training strategy with few training samples for single-shot 3D measurement. [ABSTRACT FROM AUTHOR]

Background: Aging signs are much visible on the surface of the skin that presents different changes: cheeks start to sag, more and deeper wrinkles appear, and pigmentation spots increase. Face diagnostic to recommend products includes assessing cutaneous micro‐relief or the micro‐depressive network on the face. Furthermore, there is an increasing demand for clinical and instrumental methods to prove the efficacy of anti‐aging treatments. As a result, very accurate and sensitive three‐dimensional (3D) devices are developed and validated to measure and quantify aging skin and to catch fine anti‐aging products acting on wrinkles and fine lines. Methods: AEVA‐HE, a non‐invasive 3D method based on fringe projection technology, is used to robustly characterize the skin micro‐relief from a full‐face acquisition and from multiple extracted zones of interest. In vitro and in vivo studies are conducted to assess the reproducibility of this system and its precision toward a standard fringe projection system, DermaTOP. Results: The AEVA‐HE successfully measured micro‐relief and wrinkles and demonstrated the reproducibility of measurements. AEVA‐HE parameters were found highly correlated to DermaTOP. Conclusion: The present work illustrates the performance of the AEVA‐HE device and its dedicated software kit as a precious tool for quantifying the major characteristics of wrinkles appearing with age and thus offers a high potential for assessing the effect of anti‐wrinkling products. [ABSTRACT FROM AUTHOR]

Surface defect is one of the most important indexes for quality inspection of apple fruits. However, it is always difficult to distinguish between the surface defect and the stem/calyx of apples using traditional machine vision from the color information, due to the high similarity of the intensity distribution. This study aims to prevent the disturbance of the apple stem/calyx on the surface defect recognition. An effective apple stem/calyx detection was proposed using three-step phase-shifting algorithm, in terms of the three-dimensional (3D) information instead of color information. A typical fringe projection system was built with a digital projector and an industrial camera for the apple stem/calyx detection. The digital projector was used to sequentially illuminate three phase-shifting fringe patterns onto the apple sample during image acquisition. At the same time, the industrial camera was used to synchronously capture three fringe images that modulated by the apple surface. There were the outstandingly different concavity and convexity of the modulated fringe images within the stem/calyx regions from the normal. The reason was that the stem/calyx regions on the apple surface were usually concave relative to the other normal regions. The experimental analysis showed that the modulated fringe images were bending the left within the stem/calyx regions, while bending the right within the normal regions. Three-step phase-shifting algorithm was then utilized to recover the wrapped phase of the modulated fringe images that used to indicate the bending direction. Several shifted wrapped phases were computed from the original wrapped phase with the assistance of the remainder operation. The binary fringes were then obtained by simply applying threshold segmentation on these shifted wrapped phases. The convex residuals on the right side of these binary fringes were extracted using connected component labeling and two-dimensional convex hull algorithms. As such, the entire region of apple stem/calyx was detected to combine all convex residuals of these binary fringes. The total 684 group of modulated fringe images were captured from the apple samples to detect the stem/calyx of different apples with the different sizes, colors or poses, in order to validate the performance of the improved model. These modulated fringe images were mainly divided into four types: normal apple with stem/calyx, normal apple without stem/calyx, defective apple with stem/calyx, and defective apple without stem/calyx. The experiment results indicated that the proposed detection was correctly identified the apple stem/calyx under different positions and angles, even though the apple samples with the outstanding surface defects. Statistical results showed that the overall recognition rate reached 99.12%, and the recognition rate were all beyond 98.30% for the four types of the modulated fringe images. In addition, the average processing time was only about 0.479 s on MATLAB platform, fully meeting the requirement of online detection. Compared with the traditional machine vision using color information, the wrapped phase relating to 3D information was very insensitive to the surface color, indicating more suitable to distinguish the surface defect and the stem/calyx of apples. Moreover, the three fringe patterns were only required to recover the wrapped phase, compared with traditional structured light that require to reconstruct the 3D information of the apple surface. Consequently, the better performance was achieved in the higher recognition rate, the faster image acquisition speed, and the less duration of image processing. The great potential can be expected as the simple setup, high accuracy, and high speed for the stem/calyx detection of various fruits, such as apples, pears, and peaches. The finding can also provide the technical support for the quality inspection of apple surface. [ABSTRACT FROM AUTHOR]

Composite pressure vessels (CPVs) have become the main equipment for hydrogen storage; however, the effect of defect in the laminates of CPVs is difficult to detect. In this paper, composite specimens containing wrinkle defects were investigated, and a heterogeneity model of a wrinkle defect is proposed. A three-dimensional finite element code was developed to predict the behavior of carbon fiber-reinforced composite laminates with wrinkle defects. The effect of the geometric asymmetry of clamping was distinguished from the whole response. It was found that wrinkle defects are sensitive to tension but completely insensitive to torsion and that the distortion of out-of-plane displacement is strongly dependent upon wrinkle defects. An optical–mechanical method based on fringe projection was presented to measure the response of wrinkle defects, which shows an outstanding performance on wrinkle location and deformation visualization. [ABSTRACT FROM AUTHOR]

Structured light fringe projection profilometry (FPP) has been widely researched and applied in industrial manufacturing, medical imaging, virtual reality, etc. However, the acquisition of high-quality fringe datasets and the depth prediction of abrupt depth regions at the edges of the single-shot fringe image are still tricky challenges. Therefore, we build a virtual FPP system to generate the desired datasets conveniently and simply based on accurately pre-calibrating intrinsics and extrinsics of the actual structured light system and propose a Multi-scale Fusion Dual Attention Mechanism Network (MFDA-Unet) for the depth prediction and 3D reconstruction of a single high-frequency fringe image, combining edge gradient enhancement with global sliding standard deviation loss function to capture the detailed information in the edge regions of the objects. The training model of the network is fed back to the actual structured light system for testing, completing high-precision 3D reconstruction for complicated objects with complex surface textures from a single fringe pattern. The mean absolute error (MAE) of our proposed MFDA-UNet network is only 0.33% for the range of object depth and the whole root mean square error (RMSE) of prediction is reduced by 28.88%. • A single-shot fringe projection profilometry accuracy improvement method is proposed. • An end-to-end deep convolutional neural network is developed for depth estimation. • A multi-scale fusion dual-attention module is proposed for capturing detailed information. • A new loss function is introduced to improve edge detection performance. • The proposed method has potential advantages for the 3D reconstruction of industrial products with complex surface textures and high dynamic range. [ABSTRACT FROM AUTHOR]

• Propose a binary-focusing fringe measurement technique to fundamentally solve the limitations of traditional defocusing projection mechanism and extend the reconstructed depth range. • Develop a cycle generative adversarial framework to achieve binary-to-sinusoidal domain fringe translation, thus improving the phase quality in the whole depth range. • Establish an unpaired dataset and utilize the cycle-consistency strategy for unpaired data learning, which reduces the dependence of conventional deep learning on paired data training. • Significantly expands the reconstructed depth range, improves the reconstruction accuracy, and shows great potential in large depth range high-speed dynamic 3D measurement. The binary-defocusing technique (BDT) shows prospects in high-speed 3D reconstruction. However, the defocusing projection mechanism results in low accuracy at inappropriate defocus levels due to the high-order harmonics, thereby constraining the achievable depth range. To overcome the problems, this paper proposes a large depth range binary-focusing projection technique, aimed at fundamentally solving the compromise of the reconstructed depth range. Based on the focusing-projection strategy, we develop a cycle generative adversarial framework for the binary-to-sinusoidal fringe domain translation to obtain high-quality phase information in both the focused and improperly defocused regions. Additionally, a dedicated unpaired dataset is built to perform self-supervised learning through the utilization of a cycle consistency strategy. Compared with the traditional BDT, the proposed method is not limited by the defocusing degree but inherits the advantages of high-speed projection, thus showing substantial potential to achieve super-fast and large depth range dynamic reconstruction and measurement. Experiments verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The accuracy of fringe projection profilometry is compromised when the system incorporates complex distortions that are challenging to accurately model. To address this issue, this paper presents a novel calibration method for fringe projection profilometry. Firstly, a pixel-wise phase-coordinate mapping model is established. Subsequently, the continuous mapping from pixels to XY coordinates is obtained by fitting a B-spline surface, and the pose estimation of calibration plate is achieved by constructing a virtual linear camera. Notably, this method avoids introducing any parameters representing the physical characteristics of lens distortion throughout the entire calibration process and eliminates the need for precision mobile equipment. Experimental results demonstrate that the proposed method exhibits significantly enhanced calibration accuracy compared to the traditional method in fringe projection profilometry system with complex lens distortions. • Effectively address the complex distortion in fringe projection profilometry. • Operate flexibly without requiring precision mobile equipment. • Use B-spline surface to represent the mapping from pixels to XY coordinates. • Construct a virtual linear camera to estimate the poses of the calibration plate. [ABSTRACT FROM AUTHOR]

Fringe projection is an important technology in three-dimensional measurement and target recognition. The measurement accuracy depends heavily on the calibration of the absolute phase and projector pixels. An easy-to-implement calibration method based on the Random Sample Consensus (RANSAC) algorithm is proposed to exterminate the phase error data and elevate the measurement accuracy in a fringe projection system. The reconstruction experiments of a double-sphere standard demonstrate that the uncertainties in radius and sphere-distance measurement are reduced to one thousandth of the measured value or even less, and the standard deviation in multiple measurements is restricted to within 50 μm. The measurement accuracy provided by the proposed RANSAC method can be improved by up to 44% compared with that provided by traditional least squared method (LSM). The proposed calibration method is easy and simple to implement, and it does not need additional hardware, but rather a calibration board.

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.

Phase measurement profilometry (PMP) is primarily employed to analyze the morphology of a functional surface with precision. Historically, one of the most complex and persistent challenges in PMP has been reducing errors stemming from inconsistent indicators at the edges of a surface. In response to this challenge, we propose an optimized error compensation methodology specifically designed to handle edge artefacts. This methodology introduces the Hilbert transform and object surface albedo as tools to detect the edges of the artefact region that need to be compensated. Moreover, we analyze the characteristics of the sinusoidal fringe waveform propagation direction and investigate the reconstruction results of the fringe vertical to the current directions to compensate for edge artefacts. The experimental results for various objects show that the optimized approach can compensate for edge artefacts by projecting in two directions and reducing the projection by half. The compensated root mean square error (RMSE) for planar objects can be reduced by over 45%.

This paper presents examples of high-speed 3D optical sensing for research and applications in the manufacturing community. Specifically, this paper will focus on the fringe projection technique as a special technology that can be extremely beneficial to manufacturing applications, given its merits of simultaneous high-speed and high-accuracy 3D surface measurements. This paper will introduce the basic principles of 3D optical sensing based on the fringe projection technique as well as the enabled manufacturing research applications, including both in-situ/in-process monitoring and post-process quality assurance.

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method. [ABSTRACT FROM AUTHOR]

We propose a method for accurately calibrating a telecentric structured light system consisting of a camera attached to a bilateral telecentric lens and a pin-hole projector. The proposed method can be split into two parts: axial calibration and transverse calibration. The first part is used for building the relationship between phase and depth by means of a planar plate with ring markers on its surface at several different positions in the measuring volume. The second part is used for establishing the relationship between transverse coordinates and pixel positions with the depth offered by a translation stage and the extracted ring centers. Compared with existing methods that require projector calibration, the proposed method can avoid a propagation of the correspondence error between the camera imaging plane and projector imaging plane, thus increasing calibration accuracy. The calibrated telecentric structured light system is further used for three-dimensional (3D) reconstructions of a planar, a ruled surface, and complex surfaces. Experimental results demonstrate that the proposed system calibration method can be used for accurate 3D measurement. [ABSTRACT FROM AUTHOR]

Due to color object surface interference, the distortion of captured sinusoidal fringe patterns in a colorful object measurement using fringe projection profilometry causes significant phase error and therefore shape measurement error. To address the problem, an intensity correction method is proposed to correct the acquired nonsinusoidal fringe of color object and aim to improve three-dimensional (3D) reconstruction accuracy. In this paper, first, the look-up tables (LUTs) and multi-fusion matrices (MFM) are established; secondly, finding the optimal indexing value to correct the color fringe pattern for improving recognition rates and sine; finally, the corrected pattern is separated into three phase-shifting fringe patterns, which can obtain demodulation phase by the three-step phase-shifting algorithm and then recover unwrapping phase. The simulation and experimental results demonstrate that the proposed method effectively reduces the phase error of color objects and achieves complete measurement of different color regions. [ABSTRACT FROM AUTHOR]

N-ary gray-level (nGL) coding strategy is an effective method for absolute phase retrieval in the fringe projection technique. However, the conventional nGL method contains many unwrapping errors at the boundaries of codewords. In addition, the number of codewords is limited in only one pattern. Consequently, this paper proposes a new gray-level coding method based on half-period coding, which can improve both these two deficiencies. Specifically, we embed every period with a 2-bit codeword, instead of a 1-bit codeword. Then, special correction and decoding methods are proposed to correct the codewords and calculate the fringe orders, respectively. The proposed method can generate n2 codewords with n gray levels in one pattern. Moreover, this method is insensitive to moderate image blurring. Various experiments demonstrate the robustness and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This study applied the finite element method (FEM) and the moiré strip projection method to evaluate the biomechanical behavior of healthy and endodontic-treated premolar teeth. The finite element method and the moiré strip projection method were applied to evaluate the influence of restored materials in association with cervical lesions and were considered as strain estimates for a tooth sample with 21 units, under loads of 25, 50, 75, and 100 N, frontal and oblique applied. The focused cases were: tooth H healthy; tooth A-MOD amalgam; tooth AL-MOD amalgam + lesion; tooth ALR-MOD amalgam + injury restored; tooth R-MOD resin; tooth RL-MOD resin + lesion; tooth RLR-MOD resin + injury restored. The results obtained by FEM simulation can be considered perfectly validated by the results presented by the experimental moiré projection analysis, demonstrating that the FEM numerical analysis can be used to evaluate the biomechanical behavior of healthy and endodontically treated teeth. Developing an alternative protocol to generate FEM three-dimensional models will lead to a ready and inexpensive tool since there is no need for costly equipment for tooth extraction prognosis. [ABSTRACT FROM AUTHOR]

Low back pain represents a major economic and societal challenge due to its high prevalence. Lumbar orthoses are one of the recommended treatments. Even if previous results showed their clinical effects, the detailed mode of action is still poorly known, making the device design difficult. A renewed instrumentation and experimental protocol should bring better insight into the lumbar brace–trunk mechanical interaction. This instrumentation should give detailed information on the basic physical or geometrical parameters: the pressure applied on the trunk, the body shape and the strain in the belt. The principal objective of this study was to propose and validate a new measurement protocol, based on pressure mapping systems and full-field shape and strain measurement. The feasibility of the protocol was tested along with its validity and repeatability. The influence of various parameters, which could cause changes in the measurements, was tested with six different belt configurations on one subject. Measurements were also performed to study the impact of posture on pressure and strain. Both pressure and strain appeared to be asymmetric from left to right. The pressure applied by the lumbar belt on the back varies with breathing and with posture. This study showed that full-field measurements were necessary to render the high variability of pressure or strain around the trunk, under recommendations of their use to guarantee a satisfying repeatability. [ABSTRACT FROM AUTHOR]

Fringe projection profilometry is widely used for the 3D measurement of real-world objects; however, quickly obtaining high-precision 3D measurements is an issue that needs to be resolved. Therefore, a novel fast 3D measurement method based on phase coding is proposed in this paper. It involves an unconstrained fast phase unwrapped method that can solve the absolute phase with only four fringe patterns and does not require any pre-acquired object information. This method uses the special relationship between one coded fringe pattern and the three other phase-shifting fringe patterns to achieve an unwrapped phase. To ensure higher measurement accuracy, a special coding sequence is designed to improve the projection frequency in the coding stage (0 , π) , which not only increases the number of code words, but also effectively improves the decoding accuracy. The high-speed and high-accuracy performance of the proposed method is successfully verified through simulations and experimentation, verifying its future applicability in the field of rapid measurement. [ABSTRACT FROM AUTHOR]

Fringe projection profilometry with temporal phase unwrapping (FPP-TPU) has been gotten more and more attention in reconstructing the object with complex surfaces. However, a general contradiction in the existing FPP-TPUs is that more auxiliary coded patterns may limit sampling rate in dynamic three-dimensional (3D) measurement. In this paper, inheriting the single-frame auxiliary pattern of the phase-shifting temporal phase unwrapping (PS-TPU), an improved phase shift coding temporal phase unwrapping with higher robustness is proposed. And new projection stratagem with phase shift coding division multiplexing (PSC-DM) is also proposed to enhance the reconstructing rate in dynamic 3D scene. Firstly, six codewords instead of the traditional eight codewords are embedded into the single-frame auxiliary coding pattern, which guarantees the decoding robustness and provides a faster decoding process with single-step direction unifying. Then, the new projection sequence stratagem is designed with every two sets of 3-step phase-shifting patterns projection followed by the single-frame phase-shifting coding pattern projection. During phase unwrapping, every adjacent three frames of phase-shifting pattern sequentially resolving a wrapped phase and a highly robust 3D reconstruction can be achieved by using the closest phase-shifting coding pattern so as to achieve phase-shifting coding division multiplexing. It can obtain higher reconstructing rate. In addition, a phase shift segmentation is used to correct mismatched errors of fringe order. The experimental results demonstrate that the proposed method can increase the reconstructing rate from the rate of 30 fps in traditional PS-TPU method to 68.57 fps at the digital light projector (DLP) projection rate of 120 fps, which shows its potential prospective in dynamic 3D shape reconstruction. [Display omitted] • Inheriting the existing single-frame auxiliary pattern method, an improved PS-TPU with higher robustness is proposed. • Six codewords are embedded into the phase shift to efficiently enhance the fringe order decoding ability. • A new projection stratagem is also proposed to improve the reconstructing rate in dynamic 3D sensing scenes. • Experimental results show that the proposed method can increase the reconstructing rate in complex dynamic measurement. [ABSTRACT FROM AUTHOR]

Structured light systems are crucial in fields requiring precise measurements, such as industrial manufacturing, due to their capability for real-time reconstructions. Existing calibration models, primarily based on stereo vision (SV) and pixel-wise approaches, face limitations in accuracy, complexity, and flexibility. These challenges stem from the inability to fully compensate for lens distortions and the errors introduced by physical calibration targets. Our work introduces a novel calibration approach using a virtual phase-to-coordinate mapping with a linear correction function, aiming to enhance accuracy and reduce complexity. This method involves traditional stereo calibration, phase processing, correction with ideal planes, and fitting a pixel-wise linear correction function. By employing virtual samples for phase-coordinate pairs and computing a pixel-wise correction, our methodology overcomes physical and numerical limitations associated with existing models. The results demonstrate superior measurement precision, robustness, and consistency, surpassing conventional stereo and polynomial regression models, both within and beyond the calibrated volume. This approach offers a significant advancement in structured light system calibration, providing a practical solution to existing challenges. • Novel calibration method with virtual mapping enhances precision and 10 reduces complexity. • Superior measurement precision outperforms established models in accuracy. • Simplified calibration process eliminates the need to accurately position physical targets. [ABSTRACT FROM AUTHOR]

Fringe projection profilometry (FPP) has been broadly applied in three-dimensional (3D) measurements, but the existing multi-shot methods, which mostly utilize phase-shifting techniques, are heavily affected by the disturbance of vibration and cannot be used in dynamic scenes. In this work, a single-shot 3D measurement method using a deep neural network named the Fringe Analysis Network (FrANet) is proposed. The FrANet is composed of a phase retrieval subnetwork, phase unwrapping subnetwork, and refinement subnetwork. The combination of multiple subnetworks can help to recover long-range information that is missing for a single U-Net. A two-stage training strategy in which the FrANet network is pre-trained using fringe pattern reprojection and fine-tuned using ground truth phase maps is designed. Such a training strategy lowers the number of ground truth phase maps in the data set, saves time during data collection, and maintains the accuracy of supervised methods in real-world setups. Experimental studies were carried out on a setup FPP system. In the test set, the mean absolute error (MAE) of the refined absolute phase maps was 0.0114 rad, and the root mean square error (RMSE) of the 3D reconstruction results was 0.67 mm. The accuracy of the proposed method in dynamic scenes was evaluated by measuring moving standard spheres. The measurement of the sphere diameter maintained a high accuracy of 84 μm at a speed of 0.759 m/s. Two-stage training only requires 8800 fringe images in data acquisition, while supervised methods require 96,000 fringe images for the same number of iterations. Ablation studies verified the effectiveness of two training stages and three subnetworks. The proposed method achieved accurate single-shot 3D measurements comparable to those obtained using supervised methods and has a high data efficiency. This enables the accurate 3D shape measurement of moving or vibrating objects in industrial manufacturing and allows for further exploration of network architecture and training strategy with few training samples for single-shot 3D measurement.

Traditional machine vision is widely used to identify apple quality, but this method finds it difficult to distinguish the apple stem and calyx from defects. To address this, we designed a new method to identify the stem and calyx of apples based on their concave shape. This method applies a fringe projection in a computer vision system of 3D reconstruction, followed by multi-threshold segmentation and a 2D convex hull technique to identify the stem and calyx. A camera and projector were used to reconstruct the 3D surface of the front half of an inspected apple. The height information for each pixel was reconstructed by a fringe projection and mathematical transformation. The 3D-reconstructed result was subjected to a multi-threshold segmentation technique and the segmentation results contained a concave feature in the curved line, representing the concave stem and calyx. The segmentation results were then subjected to a 2D convex hull technique, allowing for the identification of the stem and calyx. This method was evaluated using four groups of apples, and the proposed method is able to identify the stem and calyx with 98.93% accuracy. [ABSTRACT FROM AUTHOR]

It is difficult to detect defects such as shallow dents and rust on rolling surfaces by using the traditional vision-based surface inspection method (line light source and line array camera mode) which has a low sensitivity to these defects. This paper presents a method that introduces the fringe projection technique for traditional visual inspection devices to overcome the limitations of the traditional methods and uses deep-learning techniques for detecting defects such as cuts, abrasions, dents, and rust on the rolling surfaces of drum-shaped rollers. A new artificial-intelligence-based labeling method, namely, the Padua Incremental Mask Labeling Method, has been introduced for accelerating the calibration process used for defect detection, and based on a one-stage architecture, the You-Only-Look-Once-OurNet (YOLO-OurNet) deep-learning network has been designed for detecting the defects on the rolling surfaces of drum-shaped rollers. From the results of the experimental tests, the time required for detecting a defect has been found to be 0.024s, an accuracy rate of up to 99.2%, and the value of object detection evaluation index F1 of up to 0.988. Our method outperforms the related method on the domain of rolling surface defect detection. [ABSTRACT FROM AUTHOR]

One of the main challenges during digital post-processing of x-ray computed tomography (XCT) measurement data is the reconstruction of the surface geometry of the measured objects. Conventionally, the surface geometry is defined as an isosurface with identical greyscale values or via gradient-based surface geometry determination. However, these approaches are susceptible to measurement artefacts. Due to the complex surface geometry and rough surface, XCT measurements of additively manufactured (AM) parts are particularly prone to measurement artefacts caused by various physical effects when the x-rays penetrate the material. The irregular greyscale values at the measured surface geometry render commonly used single threshold greyscale value based isosurfaces as insufficient for representing the external and internal surface of the measured objects. This issue becomes particularly apparent when measuring multi-material objects, such as additively manufactured polymer objects with integrated radio-frequency identification tags. To address this challenge, this study presents a methodology for reliable surface geometry determination of XCT data based on previously acquired fringe projection (FP) data. For this purpose, the conventionally acquired surfaces geometries from the XCT and FP measurements are extracted, pre-processed and registered to each other before being merged into a single mesh. This merged data set is subsequently used as a starting point or reference for a locally adaptive threshold surface geometry determination algorithm, which is able to capture the surface geometry at a sub-voxel resolution. In order to validate the methodology and confirm the envisaged benefits, selected geometry elements of the resulting surface geometry from measurements samples manufactured by additive manufacturing with integrated RIFD tags are compared to coordinate measurement machine reference measurements. The results indicate a more robust surface geometry detection against artefacts especially for multi-material applications where the x-ray absorbance of the inner material is higher than the absorbance of the outer material. In summary, the main result is a systematic approach to merge FP and XCT data, in order to combine the advantages of both techniques, which leads to an artefact-reduced surface geometry determination for multi-material applications and a reduction of deviations from reference measurements compared to conventional methods by nearly 35%. [ABSTRACT FROM AUTHOR]