Background and amplitude encoded fringe patterns for 3D surface-shape measurement
TL;DR: A new fringe projection method for surface-shape measurement that uses background and amplitude encoded high-frequency fringe patterns that is able to perform 3D shape measurement with only four projected patterns and captured images, using a single camera and projector.
About: This article is published in Optics and Lasers in Engineering.The article was published on 2017-07-01. It has received 19 citations till now. The article focuses on the topics: Structured-light 3D scanner.
Citations
More filters
TL;DR: An overview of state-of-the-art phase shifting algorithms for implementing 3D surface profilometry is presented to provide a useful guide to the selection of the most appropriate phase shifting technique for a particular application.
611 citations
TL;DR: 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 as discussed by the authors .
Abstract: Abstract 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.
165 citations
14 Apr 2020
TL;DR: Zhang et al. as mentioned in this paper proposed a deep learning framework for phase analysis based on stereo phase unwrapping (SPU), which can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase.
Abstract: Fringe projection profilometry (FPP) has become a more prevalently adopted technique in intelligent manufacturing, defect detection, and some other important applications. In FPP, efficiently recovering the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training datasets, the neural network can gradually “learn” to transfer one high-frequency fringe pattern into the “physically meaningful” and “most likely” absolute phase, instead of “step by step” as in conventional approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved only on a single-frame projection. Experimental results demonstrate that compared with traditional SPU, our method can more efficiently and stably unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views. Limitations about the proposed approach are also discussed. We believe that the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated objects from a single fringe pattern.
116 citations
TL;DR: 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 as discussed by the authors .
Abstract: Abstract 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.
95 citations
TL;DR: A new scheme to determine simultaneously the optimal fringe frequency, phase-shifting steps and pattern sequence under multi-frequency TPU, robustly achieving high accuracy measurement by a minimum number of fringe frames is proposed.
Abstract: Temporal phase unwrapping (TPU) is an essential algorithm in fringe projection profilometry (FPP), especially when measuring complex objects with discontinuities and isolated surfaces. Among others, the multi-frequency TPU has been proven to be the most reliable algorithm in the presence of noise. For a practical FPP system, in order to achieve an accurate, efficient, and reliable measurement, one needs to make wise choices about three key experimental parameters: the highest fringe frequency, the phase-shifting steps, and the fringe pattern sequence. However, there was very little research on how to optimize these parameters quantitatively, especially considering all three aspects from a theoretical and analytical perspective simultaneously. In this work, we propose a new scheme to determine simultaneously the optimal fringe frequency, phase-shifting steps and pattern sequence under multi-frequency TPU, robustly achieving high accuracy measurement by a minimum number of fringe frames. Firstly, noise models regarding phase-shifting algorithms as well as 3-D coordinates are established under a projector defocusing condition, which leads to the optimal highest fringe frequency for a FPP system. Then, a new concept termed frequency-to-frame ratio (FFR) that evaluates the magnitude of the contribution of each frame for TPU is defined, on which an optimal phase-shifting combination scheme is proposed. Finally, a judgment criterion is established, which can be used to judge whether the ratio between adjacent fringe frequencies is conducive to stably and efficiently unwrapping the phase. The proposed method provides a simple and effective theoretical framework to improve the accuracy, efficiency, and robustness of a practical FPP system in actual measurement conditions. The correctness of the derived models as well as the validity of the proposed schemes have been verified through extensive simulations and experiments. Based on a normal monocular 3-D FPP hardware system, our method enables high-precision unambiguous 3-D shape measurement with the highest fringe frequency up to 180 by using only 7 fringe patterns achieving a depth precision ∼ 38μm across a field of view of 400 × 300 × 400 mm.
68 citations
References
More filters
TL;DR: The high-speed and high-resolution pattern projection capability offered by the digital light projection technology may enable new generation systems for 3D surface measurement applications that will provide much better functionality and performance than existing ones in terms of speed, accuracy, resolution, modularization, and ease of use.
Abstract: We provide a review of recent advances in 3D surface imaging technologies. We focus particularly on noncontact 3D surface measurement techniques based on structured illumination. The high-speed and high-resolution pattern projection capability offered by the digital light projection technology, together with the recent advances in imaging sensor technologies, may enable new generation systems for 3D surface measurement applications that will provide much better functionality and performance than existing ones in terms of speed, accuracy, resolution, modularization, and ease of use. Performance indexes of 3D imaging system are discussed, and various 3D surface imaging schemes are categorized, illustrated, and compared. Calibration techniques are also discussed, since they play critical roles in achieving the required precision. Numerous applications of 3D surface imaging technologies are discussed with several examples.
1,331 citations
TL;DR: This paper presents a meta-analyses of Fourier-Transform Profilometry and its applications in 3-D Shape Measurement and Surface Profile Measurement for Structured Light Pattern and 4-Core Optical-Fiber.
1,110 citations
TL;DR: The results show that the multi-frequency temporal phase unwrapping provides the best unwrapped reliability, while the multi -wavelength approach is the most susceptible to noise-induced unwrappers errors.
598 citations
TL;DR: A phase unwrapping algorithm based on the reliability-guided parameter map is reviewed, which shows that in the worse case the error is limited, if there is any, to local minimum areas.
397 citations
TL;DR: With this system, together with the fast three-step phase-shifting algorithm and parallel processing software, high-resolution, real-time 3-D shape measurement is realized at a frame rate of up to 40 frames/s and a resolution of 532×500 points per frame.
Abstract: We describe a high-resolution, real-time 3-D shape measurement system based on a digital fringe projection and phase-shifting technique. It utilizes a single-chip digital light processing projector to project computer-generated fringe patterns onto the object, and a high-speed CCD camera synchronized with the projector to acquire the fringe images at a frame rate of 120 frames/s. A color CCD camera is also used to capture images for texture mapping. Based on a three-step phase-shifting technique, each frame of the 3-D shape is reconstructed using three consecutive fringe images. Therefore the 3-D data acquisition speed of the system is 40 frames/s. With this system, together with the fast three-step phase-shifting algorithm and parallel processing software we developed, high-resolution, real-time 3-D shape measurement is realized at a frame rate of up to 40 frames/s and a resolution of 532×500 points per frame.
350 citations