The papers in this special section focus on geometric modeling and processing. The modeling and processing of geometric data are fundamental to many computer applications, including computer graphics, computer vision,...
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The papers in this special section focus on geometric modeling and processing. The modeling and processing of geometric data are fundamental to many computer applications, including computer graphics, computer vision, CAD/CAM, medical imaging, engineering analysis, robotics, additive manufacturing, and scientific computing. There has recently been increasing demand for geometric problems in various emerging application fields such as virtual reality and augmented reality (VR/AR) and robotics. All of these applications bring new geometric challenges and problems to solve.
processing the 3D objects is a research field of computer graphics that can be applied in 3D simulation, 3D image processing, game industry, etc. This is one of the mathematics-based research fields where geometrical ...
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processing the 3D objects is a research field of computer graphics that can be applied in 3D simulation, 3D image processing, game industry, etc. This is one of the mathematics-based research fields where geometrical knowledge is the background of existing methods. geometricmodeling is a branch of this research field based on the applied mathematics and computational geometry. It studies methods and solutions for the mathematical description of shapes or realistic objects in computer science or computer aided design. The rapid development of advanced techniques, like 3D scanners, can be used to support obtaining scanned data of any type of the real objects with different methods and solutions. This article investigates the studies based on the background of geometricmodeling for processing the 3D Objects. Our contribution is focused on presentation of a completed method that is an integrated approach consisting of our previously proposed methods to reconstruct the 3D object from a 3D point cloud dataset. The method consists of four main steps, as follows: (1) Obtaining data describing the 3D objects in the real world;(2) processing and simplifying the obtained data;(3) Reconstructing the 3D objects by filling the holes on the surface;and (4) Meshing and visualizing the 3D objects in the application. The comparison and discussion between the methods have shown the advantages of the proposed method and its application in practice.
This foreword provides an overview of the contributions presented at the fifteenth International Conference on geometric modeling and processing (GMP2021 -originally scheduled for May 10-13, 2021 in Pilsen, Czech Repu...
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This foreword provides an overview of the contributions presented at the fifteenth International Conference on geometric modeling and processing (GMP2021 -originally scheduled for May 10-13, 2021 in Pilsen, Czech Republic). (C) 2021 Elsevier B.V. All rights reserved.
The assembly performance of precision structural threaded connections is significantly influenced by the threads' geometric parameters and the geometric form of the thread contact surface (TCS). Due to the complex...
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The assembly performance of precision structural threaded connections is significantly influenced by the threads' geometric parameters and the geometric form of the thread contact surface (TCS). Due to the complexity involved in the helical structure and surface form of the thread itself, establishing a 3D solid model that conforms to the actual machining conditions presents challenges. This study first proposes an accurate geometric error 3D modeling method for representing the non-uniform distribution error of the TCS. The non-contact optical measurement method obtained the point cloud data containing the actual machining errors of the TCSs. Based on data processing, a 3D distribution error model of the TCS was established using the NURBS surface numerical model. Using CAD modeling, the 3D geometric distribution error was integrated with the ideal 3D solid model to create an accurate geometric digital twin model of the thread. Then, anon- uniform distribution error evaluation method of TCSs was proposed to quantitatively evaluate the non-uniform distribution characteristics of the errors on the TCSs. The feasibility of the proposed method was validated by comparing the modeling and evaluation of normal-precision and high-precision threads. The simulation calculations results of ideal and error bolted joints indicated that the non-uniform distribution errors on the TCS lead to local point contact or non-contact, and the resulting stress distribution is discontinuous and nonuniform. This study provides a practical approach for predicting and optimizing the performance of threaded connections.
A hybrid model called the geometric (Clifford) quanvolutional neural network ( GQNN ) that merges elements of geometric (Clifford) convolutional neural networks (GCNNs) and variational quantum circuits (VQCs) is prese...
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A hybrid model called the geometric (Clifford) quanvolutional neural network ( GQNN ) that merges elements of geometric (Clifford) convolutional neural networks (GCNNs) and variational quantum circuits (VQCs) is presented. In this model, a randomized quantum convolution operation is applied to the input image, giving as a result four output channels, which are treated as a single entity (quaternion image) by the subsequent quaternion layers. This approach is extended to Clifford algebras by choosing the number of qubits of the quantum circuit according to the dimension of the Clifford algebra so that the resulting output channels are regarded as the components of a multivector image to be further processed by Clifford layers.
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