Article
Engineering, Multidisciplinary
Yang Zhao, Rongbiao Zhu, Kai Zhang, Haotian Yu, Lianfa Bai, Dongliang Zheng, Jing Han
Summary: This paper proposes a new method for dynamic 3-D measurement using the inverse relationship between camera sampling speed and sampling resolution and image super-reconstruction technique. The method improves measurement speed and eliminates errors caused by dynamic motion.
Article
Optics
Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo
Summary: This paper presents a method based on deep neural networks to directly recover the absolute phase of isolated objects from a single fringe image, enabling high-quality 3D reconstructions within a single image.
OPTO-ELECTRONIC ADVANCES
(2022)
Article
Optics
Yanjun Fu, Yiliang Huang, Wei Xiao, Fangfang Li, Yunzhan Li, Pengfei Zuo
Summary: This paper combines deep learning with binocular fringe projection for 3D reconstruction. By using unwrapping and matching disparity maps, the 3D shape of the object is restored, and the feasibility of this approach is demonstrated through experimental results.
OPTICS AND LASERS IN ENGINEERING
(2024)
Article
Chemistry, Analytical
Min Xu, Yu Zhang, Yingying Wan, Lin Luo, Jianping Peng
Summary: A single-shot multi-frequency absolute phase retrieval (SAPR-DL) method based on deep learning is proposed, which enables the accurate absolute phase retrieval with only one fringe image. Experimental results demonstrate that the SAPR-DL method can achieve three-dimensional shape measurement of multiple complex objects by capturing a single-shot fringe image, showing promising applications in scientific and engineering fields.
Article
Optics
Yang Zhao, Haotian Yu, Lianfa Bai, Dongliang Zheng, Jing Han
Summary: This paper investigates the influence of random phase errors caused by fluctuations of the projection light source on system calibration and 3D reconstruction in fringe projection profilometry (FPP). An accurate FPP method is proposed, which uses a deep learning-based phase calculation approach for precise calibration and 3D reconstruction, leading to a significant improvement in measurement accuracy.
OPTICS COMMUNICATIONS
(2022)
Article
Optics
Suqin Wang, Taiqin Chen, Min Shi, Dengmin Zhu, Jia Wang
Summary: This paper introduces a neural convolutional network named VRNet which achieves accurate and single-frequency phase unwrapping without extra cameras. The network obtains multi-scale feature maps by feeding the wrapped phase map into the encoder and fuses the feature maps recursively using the proposed feature fusion module. Additionally, the paper presents a phase correction method based on the distribution characteristics of the absolute phase to further improve the accuracy of phase unwrapping.
OPTICS AND LASERS IN ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Yueyang Li, Wenbo Guo, Junfei Shen, Zhoujie Wu, Qican Zhang
Summary: In this paper, a method based on three-stream neural networks is proposed to reduce motion-induced error, and a general dataset establishment method for dynamic scenes is introduced. Experimental results demonstrate that the proposed method significantly improves the traditional phase-shifting algorithm and is promising for high-speed 3D measurement.
APPLIED SCIENCES-BASEL
(2022)
Article
Optics
Pengcheng Yao, Shaoyan Gai, Feipeng Da
Summary: A method combining neural network and code-based patterns is proposed in this paper for efficiently recovering the absolute phase with high accuracy. Through a small number of patterns, high-precision measurement of complex texture objects can be achieved.
OPTICS COMMUNICATIONS
(2021)
Article
Optics
Pengcheng Yao, Shaoyan Gai, Feipeng Da
Summary: A multi-purpose neural network combined with code-based patterns is proposed to efficiently recover absolute phase and decrease pattern quantity, showing potential for high accuracy in complex texture objects.
OPTICS COMMUNICATIONS
(2021)
Article
Optics
Yunfan Wang, Huijie Zhao, Hongzhi Jiang, Xudong Li, Yuxi Li, Yang Xu
Summary: A new method for axial 3D shape measurement using parallel single-pixel imaging is proposed in this paper, encoding depth through the projection of light transport coefficients and using third-order polynomial fitting for depth mapping and calibration. Experimental results demonstrate that the method can achieve robust, dense reconstruction with high depth accuracy and accuracy.
Article
Optics
Yuzhou Chen, Jiawei Shang, Jianhui Nie
Summary: This study proposes a new method, TPNet, to effectively extract the wrapped phase from fringe patterns under non-ideal conditions. TPNet uses a neural network to predict the wrapped phase in the form of sine and cosine values, and calculates the wrapped phase using the a tan function. This approach avoids the direct processing of abrupt data by the neural network and facilitates network convergence due to its similar distribution law as fringe patterns. By introducing a new loss function, Loss(sincos), the accuracy of the neural network in indirectly predicting the wrapped phase is further improved. Experiment results demonstrate that TPNet can accurately extract the wrapped phase from single frame fringe patterns under non-ideal conditions.
OPTICAL ENGINEERING
(2023)
Article
Optics
Haotian Yu, Xiaoyu Chen, Yucheng Zheng, Lianfa Bai, Dongliang Zheng, Jing Han
Summary: In this paper, a deep learning-based fringe-enhancing method (DFEM) is proposed to improve the accuracy of 3-D reconstruction in large depth-of-field DFPP. The DFEM divides multiple sub-DoFs for pattern transformation in training and introduces geometric constraint for determining object location in testing. The experiments demonstrate the improved performance of DFPP with a larger depth-of-field.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Optics
Xiaolong Luo, Wanzhong Song, Songlin Bai, Yu Li, Zhihe Zhao
Summary: This paper proposes a hybrid spatial phase unwrapping method that combines deep learning-enabled invalid-point removal and traditional path-following. The method demonstrates better robustness than traditional quality-guided methods, better interpretability than end-to-end deep learning schemes, and generality on unseen data. Experiments on a real dataset validate the effectiveness of the proposed method.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Optics
Liming Chen, Xiaowei Hu, Song Zhang
Summary: This paper presents a calibration method for a microscopic structured light system with an extended depth of field. The method employs the focal sweep technique to achieve a large depth measurement range and develops a computational framework to alleviate phase errors caused by the calibration target. Experimental results demonstrate high measurement accuracy in a large measurement volume.
Article
Optics
Vaishnavi Ravi, Rama Krishna Gorthi
Summary: 3D reconstruction by fringe projection is an ill-posed problem. In this study, a Circular Fringe-to-3D reconstruction Network (CF3DNet) is proposed, which can establish a one-to-one mapping between phase deformations and absolute phase shifts. It can reconstruct discontinuous object profiles with the help of radially symmetric circular fringes.
OPTICS AND LASERS IN ENGINEERING
(2023)
Article
Engineering, Manufacturing
Xiao Zhang, Yi Zheng, Vignesh Suresh, Shaodong Wang, Qing Li, Beiwen Li, Hantang Qin
JOURNAL OF MANUFACTURING PROCESSES
(2020)
Correction
Optics
Yi Zheng, Xiao Zhang, Shaodong Wang, Qing Li, Hantang Qin, Beiwen Li
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Optics
Lu Li, Yi Zheng, Kun Yang, Xin Su, Yuwei Wang, Xiangcheng Chen, Yajun Wang, Beiwen Li
Summary: A dynamic 3D reconstruction framework based on a modified algorithm is proposed to improve the speed and accuracy of dynamic 3D shape measurement by reducing the number of measurement patterns and using phase error compensation method. Experiments demonstrate the effectiveness of the proposed framework in improving the speed of dynamic 3D shape measurement by 1/3.
OPTICS COMMUNICATIONS
(2021)
Article
Engineering, Multidisciplinary
Vignesh Suresh, Yi Zheng, Beiwen Li
Summary: Fringe projection profilometry (FPP) is a 3D shape measurement method involving projecting fringe patterns onto objects, with Fourier transform and phase-shifting being widely used for analysis. A deep learning method proposed in this research, using a model called PMENet, successfully enhances the quality of FTP phase maps, reducing artifacts and noise in the reconstructed 3D surfaces.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Shaodong Wang, Xiao Zhang, Yi Zheng, Beiwen Li, Hantang Qin, Qing Li
Summary: The study establishes a comprehensive and flexible framework for surface topography data comparison, which generates a similarity score and has the potential to improve the quality assurance cycle in additive manufacturing processes.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Review
Optics
Lei Lu, Vignesh Suresh, Yi Zheng, Yajun Wang, Jiangtao Xi, Beiwen Li
Summary: Researchers have extensively studied the use of phase shifting profilometry (PSP) for 3D shape measurement and have proposed various motion-induced error reduction methods. Experimental comparisons were conducted to evaluate the effectiveness of different algorithms in compensating errors, and comparative discussions on method selection in different application scenarios were provided.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Yiqun Jiang, Qing Li, Giovani Trevisan, Daniel C. L. Linhares, Cameron MacKenzie
Summary: This research investigates the relationship between PRRSV detection in two age categories: wean-to-market and adult/sow farm, and examines the predictive power of wean-to-market PRRSV positive rate on adult/sow farm PRRSV positive rate. The study finds that an increase in wean-to-market PRRSV positive submissions precedes an increase in adult/sow farms to a large extent. The analysis using control charts shows that 78% of PRRSV signals in wean-to-market are followed by PRRSV rate signals in adult/sow farms within eight weeks.
Article
Engineering, Multidisciplinary
Yiqun Jiang, Shaodong Wang, Hantang Qin, Beiwen Li, Qing Li
Summary: This research establishes a framework to quantify the similarity of 3D surface topography measurements and determine whether they are from the same surface in the frequency domain after 2D Fourier transformation. It provides a new perspective for surface topography similarity evaluation and serves as a benchmark work in 3D surface topography feature extraction. The work has the potential to benefit quality assurance in additive manufacturing and other fields where surface topography data is valuable.
Article
Optics
Jiaqiong Li, Yi Zheng, Lingling Liu, Beiwen Li
Summary: This paper presents a 4D line-scan hyperspectral imager that integrates 3D geometrical measurement and spectral detection with high spectral resolution and spatial accuracy. The system achieves a spectral resolution of 2.8 nm and a spatial root-mean-square-error of 0.0895 mm. It demonstrates potential applications in the food industry, such as inspecting the quality and defects of spinach leaves based on spectral and depth data.
Proceedings Paper
Engineering, Electrical & Electronic
Yi Zheng, Beiwen Li
Summary: The study proposes a framework to establish the digital twin of a real-world system in a virtual environment and a process to generate 3D training data automatically. Experiments demonstrate that a physical system can adopt the CNN trained in the virtual environment to perform accurate real-world 3D shape measurements.
EMERGING DIGITAL MICROMIRROR DEVICE BASED SYSTEMS AND APPLICATIONS XIII
(2021)
Article
Engineering, Manufacturing
Yi Zheng, Beiwen Li
JOURNAL OF MICRO AND NANO-MANUFACTURING
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Yi Zheng, Yajun Wang, Beiwen Li
DIMENSIONAL OPTICAL METROLOGY AND INSPECTION FOR PRACTICAL APPLICATIONS IX
(2020)
