Three-dimensional reconstruction of welding pool surface by binocular vision

Gu, Zunan; Chen, Ji; Chen, Wu

doi:10.21203/rs.3.rs-19923/v1

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…3D reconstruction technology is one of the important branches of computer vision technology (Figure 1), and it is a popular research field combining computer vision and industrial measurement. Among them, in the fields of rapid design of industrial products, automatic detection and measurement, quality inspection and control, 3D printing, and other fields, the demand for fast, accurate, and convenient acquisition of 3D information on the surface of objects is increasing [2].…”

Section: Introductionmentioning

confidence: 99%

Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm

Huang

2022

Scanning

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In order to solve the problem of multisolution and ill-formedness of the 3D reconstruction method of a single image (purpose), the author proposes a microscope image segmentation algorithm based on the Harris multiscale corner detection. Separating complex engineering images into several simple basic geometric shapes, rebuild them separately to avoid the ill-conditioned solution problem of directly recovering depth information. In order to improve the registration accuracy of the corner-based image registration algorithm, the idea of multiresolution analysis was introduced into the classic Harris corner detection, and a gray intensity variation formula based on the wavelet transform was constructed, and a scale transformation characteristic was obtained so that the improved Harris corner detection algorithm is invariant to rotation, translation, and scale. Experimental results show that after reconstruction, the error between the length of the object measured based on the point cloud data and the actual length of the object is small, and both remain within the error range of 3 mm. The experiment verifies the fast, accurate, and stable characteristics of the improved algorithm.

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Section: Introductionmentioning

confidence: 99%

Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm

Huang

2022

Scanning

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“…It involves taking some raw data (e.g., at images) as input and converting them into a mask with highlighted regions of interest. What is evaluated is the possibility that the feature of pixel i belongs to the category label [22]. It is composed of the sum of two data items, the appearance model and the geometric model, which are obtained by the color information and the depth map information obtained by the depth camera:…”

Section: Interactive Image Semantic Segmentation Andmentioning

confidence: 99%

3D Reconstruction Method of Virtual and Real Fusion Based on Machine Learning

Zhu

Zhou

2022

Mathematical Problems in Engineering

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With the continuous development of computer vision technology, people are paying more and more attention to the method of using computers to simulate actual 3D scenes, and the requirements for 3D reconstruction technology are getting higher and higher. Virtual and real fusion refers to combining the virtual environment generated by the computer with the actual scenes around the user through photoelectric display, sensors, computer graphics, multimedia, and other technologies. This is a technology that can obtain more convenient and direct expressions, and it is also a technique for expressing content more abundantly and accurately. The key to virtual and real fusion technology is the registration of virtual objects and real scenes. It means that the system should be able to correctly estimate the position and posture of the camera in the real world, and then place the virtual object where it should be. Machine learning is a multifield interdisciplinary subject that specializes in how computers simulate or realize human learning behaviors. It is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are in all the fields of artificial intelligence. This article introduces the virtual-real fusion 3D reconstruction method based on machine learning, compares the performance of the method with other algorithms through experiments, and draws the following conclusion: the algorithm in this study is the fastest, with an average speed of 72.9% under different times. To evaluate the image acquisition indicators of each algorithm, the algorithm in this study has the lowest error rate. The matching accuracy of each algorithm is tested, and it is found that the average matching accuracy of the algorithm in this study is about 0.87, which is the highest.

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“…e technology of fully digital 3D reconstruction has been recognized and promoted in 3D models [4][5][6]. Because there is currently no system that supports fully digital stereo 3D reconstruction, detection, and mapping, the current use of fully digital stereo 3D reconstruction is to switch and combine multiple systems to assist in completing the task, which hinders the surveying and mapping work and reduces work e ciency.…”

Section: Introductionmentioning

confidence: 99%

Panoramic Image Generation Technology for Full Digital Stereo 3D Reconstruction in Water Conservancy Surveying and Mapping Engineering

Mao

Zhao

2022

Mobile Information Systems

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Due to the lack of image feature extraction in traditional panoramic image generation technology, the effect of 3D reconstruction of physical objects is poor. In this paper, fully digital stereoscopic 3D reconstruction technology is introduced in water conservancy surveying and mapping engineering to ensure complete panoramic image generation. Through in-depth analysis of the principle of 3D reconstruction technology and the database, the detailed water conservancy surveying and mapping engineering images are obtained, the feature vectors of the obtained panoramic images are matched, and the database is compared and positioned according to the matched position features. By using the initial position camera and calibration features, comparison and query are conducted with the image database, to remove the worthless confidence data, retain the outline of the object with reference value, obtain a fixed pose center point, and remove the overlapping area in the process of 3D reconstruction after calculating the outline of the image. Finally, the automatic generation of panoramic images is realized. Finally, the results of experimental analysis show that the panoramic image generation technology of the fully digital stereoscopic 3D reconstruction proposed in this paper has a relative accuracy of 49% compared with the traditional image generation method, and the effect of image generation is relatively good, which has a certain use value.

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Three-dimensional reconstruction of welding pool surface by binocular vision

Cited by 4 publications

References 20 publications

Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm

Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm

3D Reconstruction Method of Virtual and Real Fusion Based on Machine Learning

Panoramic Image Generation Technology for Full Digital Stereo 3D Reconstruction in Water Conservancy Surveying and Mapping Engineering

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