In this paper, we propose a method to estimate normal vectors based on neighborhood clustering segmentation, which improves the accuracy of normal-vector estimation for sharp features. The proposed method adjusts the neighborhood through Gauss mapping and clustering segmentation to solve the problem of inaccurate estimation of the normal vector in the sharp-feature region. First, the normal vectors of the point cloud are initially estimated by principal component analysis (PCA). Next, the neighborhood of points from different patches, which are close to the sharp feature, are mapped to a unit Gauss sphere, and the point cloud on the unit sphere is clustered. All points of the cluster belonging to the target point are divided into sub-neighborhoods. Finally, with this sub-neighborhood as input, the normal vector of the point is accurately calculated by PCA. Experiments show that, even for noise and non-uniform sampling, the method proposed herein outperforms previous methods in terms of quality of results and running time.
As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.
In this paper, a method of reconstructing 3D (three dimensional) models from the original scanned point cloud using priori templates is proposed. Different from previous reconstruction methods that triangulate and fit the original scanning point cloud directly, we construct a priori template based on the CAD (computer aided design) model and guide the reconstruction of the original scanning point cloud with the priori template. Given a CAD model, the basic geometric elements are used as the basic units to extract the set elements of 3D shapes. Then the geometric elements are meshed, and the normal vectors at the mesh nodes are extracted. The corresponding point cloud data of each basic element are extracted from the original point cloud. The point cloud data near the normal of the guide point are searched, and the Gaussian weighted average value of the searched point represents the actual geometric parameters of the part at the guide point. Finally, the geometric elements of the basic unit are reconstructed locally by Non-Uniform Rational B-Splines surface fitting, and the complete reconstruction model is obtained by integrating the local reconstruction. Experiments show that our method can solve the problems of high quality reconstruction, sharp feature preservation, and detail recovery in surface reconstruction.
Compared with the traditional assembly simulation based on theoretical models, this paper proposes a new pre-assembly analysis method of aircraft components based on measured data. Specifically, before the actual assembly of the product, digital measurement methods are used to obtain the measured data of the target features of the manufactured parts. Subsequently, the measured data is processed and reconstructed to obtain the actual geometric shape of the part, based on which the product is pre-assembled and analyzed to evaluate the assembly quality in advance. Finally, according to the analysis results, the assembly process is adjusted in time to reduce assembly trial and error and improve assembly quality and efficiency. This article systematically introduces the implementation process of the method, which is illustrated through two cases study on aircraft wing box assembly process. Experimental results demonstrate the feasibility and effectiveness of this proposed method for assembly of large aircraft components.
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