Projector distortion correction in 3D shape measurement using a structured-light system by deep neural networks

Lv, Shenzhen; Sun, Quan; Zhang, Yuyuan; Jiang, Yang; Yang, Jianbai; Liu, Jianzhuo; Wang, Jian

doi:10.1364/ol.45.000204

Cited by 24 publications

(7 citation statements)

References 13 publications

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“…In addition, the deep-learning-based technique was fully automatic, and the robustness and accuracy were shown to be superior to PCA. Lv et al 376 used DNN to compensate projector distortion-induced measurement errors in a FPP system. By learning the mapping between the 3D coordinates of the object and their corresponding distortion-induced error distribution, the distortion errors of the original test 3D data can be accurately predicted.…”

Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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

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Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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“…Many projectors are designed to project off-axis and their principal points are often outside imaging planes. As a result, residual distortion is usually observed [20,21], despite the use of depth-dependent pinhole models [22]. For example, Yang et al [9] compensates residual distortion using a calibrated lookup table.…”

Section: Projector Distortion Modelmentioning

confidence: 99%

Projector undistortion for high-accuracy fringe projection profilometry

Cao

Yu³

et al. 2021

Meas. Sci. Technol.

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Nowadays, fringe projection profilometry is widely used in optical three-dimensional (3D) measurement. However, due to distortion, epipolar constraints are inaccurate for use in correspondence matching of a projector. This results in low accuracy of final 3D reconstruction. To address the issue, two simple and effective methods for undistorted correspondence matching are proposed in this paper. The first removes distortion from projector correspondences using double epipolar constraints; the second pre-distorts the fringe images for projection, thus the projected fringes become naturally undistorted. Comparative experiments are implemented, in a measurement volume of (50 × 30 × 8) mm. The results show that typical mean reconstruction errors of the two methods drop to 32.9% and 27.6% from that of an existing method based on single epipolar-constraint undistortion.

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“…In this paper, we adopt Zhang and Huang's method [25] for system calibration, in which the projector is regarded as an inverse camera. This method has the advantages of simplicity, simultaneity and high accuracy, and has become a popular calibration method [26]. In this calibration method, a linear pinhole model is used to describe the imaging process of the camera in which the relation between a point of (x w , y w , z w ) on the object and its projection of (u c , v c ) on the image sensor can be written as…”

Section: Calibration Of the Systemmentioning

confidence: 99%

Geometric dimension assisted absolute phase recovery in 3D shape measurement with digital fringe projection

Zhang

Jing

et al. 2021

Meas. Sci. Technol.

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A novel method of geometric dimension assisted absolute phase recovery in 3D shape measurement is presented. This method mainly includes two steps: (a) using the window Fourier filter-quality guided phase unwrapping algorithm to obtain the relative phase distribution of the tested object; (b) using the geometric dimension of the object as a clue to convert the relative phase distribution to the absolute phase distribution. This method is convenient, and only three fringe image acquisitions are needed to recover the absolute phase. In addition, there is no limit to the depth range of the tested object. The correctness of this method is verified by several experiments.

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Projector distortion correction in 3D shape measurement using a structured-light system by deep neural networks

Cited by 24 publications

References 13 publications

Deep learning in optical metrology: a review

Deep learning in optical metrology: a review

Projector undistortion for high-accuracy fringe projection profilometry

Geometric dimension assisted absolute phase recovery in 3D shape measurement with digital fringe projection

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