Research on 3D model reconstruction based on a sequence of cross-sectional images

Dong, Zhiguo; Wu, Xiaobo; Ma, Zuwei

doi:10.1007/s00138-021-01220-7

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Based on infrared technology, literature realized automatic recognition of hot spots by using the method of information fusion and fuzzy reasoning [11]. Reference reports that using prior knowledge to segment the panel area of photovoltaic hot spot image, can effectively avoid interference caused by background information on hot spot recognition and improve the subsequent recognition accuracy [12].…”

Section: Y Jin Et Almentioning

confidence: 99%

Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching

Jin,

Yu,

Yin

et al. 2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.

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Section: Y Jin Et Almentioning

confidence: 99%

Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching

Jin,

Yu,

Yin

et al. 2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

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“…Reverse engineering technology is widely used in modern industrial design, material production processes, product modeling and analysis, and other advanced manufacturing fields [1][2][3]. Three-dimensional (3D) reconstruction is an important research topic in reverse engineering technology [4,5]. In order to improve the accuracy of reconstruction and improve the operation efficiency of algorithms, many researchers have conducted in-depth exploration and research on 3D laser scanning systems [6], and point cloud registration technology, as an important part of 3D reconstruction and laser scanning, has become a hot topic for many scholars [7,8].…”

Section: Introductionmentioning

confidence: 99%

Coarse–Fine Registration of Point Cloud Based on New Improved Whale Optimization Algorithm and Iterative Closest Point Algorithm

Tian,

Yue,

Zhu

2023

Symmetry

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In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimization algorithm (WOA). Firstly, circle chaotic mapping is used to initialize the whale population to increase the diversity of the population and make the search space more comprehensively explored. In addition, a Newton inertia weight is proposed to flexibly adjust the proportion of global exploration and local optimization in order to achieve the balance between the exploitation performance and exploration ability of the algorithm. At the same time, we introduce the nonlinear convergence factor that can adjust the size adaptively so that the algorithm can find the global optimal solution faster and more accurately, allowing it to avoid falling into the local optimal solution to a certain extent. The NIWOA algorithm is used to optimize the objective function of point cloud coarse registration to obtain the optimal coordinate transformation, and the rotation and translation operation is carried out on the registered point cloud. The obtained position is used as the initial pose of the ICP fine registration, and the final registration is achieved through ICP iteration. We conduct coarse registration experiments on multiple model point clouds and scene point clouds using the Stanford 3D Scanning Repository dataset and Princeton 3Dmatch dataset, respectively. The experimental results confirm that the NIWOA algorithm can not only find the initial position that is closer to the target point cloud, but also provide reliable initial values for the ICP algorithm. Meanwhile, the NIWOA algorithm combined with ICP experiment results show that the method has a higher registration accuracy and operation efficiency.

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