Three-Dimensional Microscopic Image Reconstruction Based on Structured Light Illumination

Shi, Taichu; Yang, Qi; Zhu, Cheng; Tang, Ying; Wu, Bian

doi:10.3390/s21186097

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…Optical 3D measurement technology has the advantages of being noncontact, having high precision and fast speed, and being applicable to static and dynamic measurements; it is widely used in fields such as machine vision, industrial inspection, biometrics, and reverse engineering 1 – 4 Binocular vision and structured light are important methods of optical 3D measurement technology.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional reconstruction based on binocular structured light with an error point filtering strategy

Li,

Yang,

Wan

et al. 2024

Opt. Eng.

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Gray code assisted phase shifting technology can achieve robust and noise-tolerated three-dimensional (3D) shape measurements. To solve the issues of unsynchronized brightness changes, local overexposure, and edge coding errors caused by inconsistent reflectivity of the surface in complex industrial scenes, as well as defocusing caused by noncontinuous surfaces and varying distances, we combine the advantages of a large imaging range in passive stereo vision and high precision in active structured light imaging. It uses a consumer-grade projector to project gray code and stripe patterns, whereas two precalibrated color industrial cameras capture raw images and obtain the original channel data. Gray code and reverse gray code images are projected to solve the problems of binarization and boundary blur. In addition, an error point filtering strategy is proposed to retain pixels with decoding errors of less than two bits. The use of softargmin for subpixel matching of absolute phase results in a high precision disparity map. We present a simple and high precision 3D measurement system for industrial objects. Experiments on 3D measurements in complex industrial scenes showed that the proposed method can achieve high precision and robust 3D shape measurements.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional reconstruction based on binocular structured light with an error point filtering strategy

Li,

Yang,

Wan

et al. 2024

Opt. Eng.

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“…Since the milling tool diameter between 0.5mm~25mm is normally utilized in high-precision machining, the 3D reconstruction of the milling tool tip belongs to the field of 3D reconstruction of microscopic objects. Currently, the 3D reconstruction methods of microscopic objects mainly include structured light 3D reconstruction [4], laser scanning confocal 3D reconstruction [5], and SFF [6]. The principle of structured light 3D reconstruction is to calculate the depth information by observing the projection-fringe's deformation caused by the uneven surface of the object.…”

Section: Introductionmentioning

confidence: 99%

Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

Peng

Qin

Wang

et al. 2023

Int J Adv Manuf Technol

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In precision machining, the milling tool' geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence's pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace 2 algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evalution operators of SFF, the 3D point cloud obtained by this paper's proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes. In addition, compared with Genmagic software, the 3D point cloud volume calculation method adopted in this paper could accurately calculate the 3D point cloud volume of the milling tool tip, and the percentage error was less than 1%.

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A Robust End-to-End Speckle Stereo Matching Network for Industrial Scenes

Liu,

Yang,

et al. 2024

IEEE Access

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The detection capability of deep learning-based stereo matching in industrial applications is inherently limited due to challenges posed by weak texture and inconsistent reflectance, making it difficult to accurately recover complex surface details. To achieve accurate measurements, this paper presents an endto-end speckle stereo matching network that incorporates fringe, Gray code, and speckle projection patterns. The model is trained using a high-precision dataset consisting of thousands of pairs generated through binocular Gray code-assisted phase shifting. After establishing local correspondences between the left and right images using speckle patterns, the images are used as inputs to the network. The proposed network consists of two siamese 2D feature extraction networks. One network is dedicated to cost volume computation, while the other focuses on weight refinement feature extraction. The former network incorporates a lightweight module for extracting high-dimensional fusion features. These features are obtained from different dilation scales and randomly concatenated along the channel dimension. Patch convolution is utilized to effectively adapt to pixel features at various levels, reducing redundancy within the cost volume and improving the network's capacity to learn from ill-posed regions. Experimental results demonstrate that the proposed network achieves a significant improvement of approximately 10.7% in matching accuracy compared to state-of-the-art networks on public datasets. Furthermore, this method exhibits outstanding matching results when applied to diverse industrial scenarios. The reconstruction error for the radius of optical standard spheres is below 0.06mm, which meets the demands of the majority of industrial applications.INDEX TERMS Stereo matching, Gray code-assisted phase shifting, high robustness industrial imaging, group-wise volume & correction map

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Three-Dimensional Microscopic Image Reconstruction Based on Structured Light Illumination

Cited by 6 publications

References 33 publications

Three-dimensional reconstruction based on binocular structured light with an error point filtering strategy

Three-dimensional reconstruction based on binocular structured light with an error point filtering strategy

Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

A Robust End-to-End Speckle Stereo Matching Network for Industrial Scenes

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