参数化CAD模型的多视图重构

IF 3.1 Q2 ENGINEERING, INDUSTRIAL
Rubin Fan, Yi Zhang, Fazhi He
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引用次数: 0

摘要

计算机辅助设计(CAD)是工程师和设计师必不可少的不可替代的工具,可以优化设计工作流程并推动各行各业的创新。然而,掌握这些复杂的CAD程序需要从业员进行大量的培训和专业知识。为了解决这些问题,本文介绍了一种多视图重构CAD模型的框架。具体来说,我们提出了一种新的端到端神经网络,能够直接从多视图重构参数化CAD命令序列。随后,该网络解决了传统神经网络注意机制固有的低秩瓶颈。最后,我们提出了一种新的参数化CAD数据集,该数据集结合了相应CAD序列的多视图,同时消除了冗余数据。对比实验表明,该框架能有效地重建高质量的参数化CAD模型,并可在协同CAD/CAM环境中轻松编辑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multiview Reconstruction of Parametric CAD Models

Multiview Reconstruction of Parametric CAD Models

Multiview Reconstruction of Parametric CAD Models

Multiview Reconstruction of Parametric CAD Models

Multiview Reconstruction of Parametric CAD Models

Computer-aided design (CAD) serves as an essential and irreplaceable tool for engineers and designers, optimising design workflows and driving innovation across diverse industries. Nevertheless, mastering these sophisticated CAD programmes requires substantial training and expertise from practitioners. To address these challenges, this paper introduces a framework for reconstructing CAD models from multiview. Specifically, we present a novel end-to-end neural network capable of directly reconstructing parametric CAD command sequences from multiview. Subsequently, the proposed network addresses the low-rank bottleneck inherent in traditional attention mechanisms of neural networks. Finally, we present a novel parametric CAD dataset that incorporates multiview for corresponding CAD sequences while eliminating redundant data. Comparative experiments reveal that the proposed framework effectively reconstructs high-quality parametric CAD models, which are readily editable in collaborative CAD/CAM environments.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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