Exploration of CAD Virtual Reality Interactive Interface Design based on Deep Convolution Neural Networks

被引：0

作者：

Chen T. ^{[1
]}

Zhang J. ^{[2
]}

机构：

[1] School of Arts, Tianjin University of Technology and Education, Tianjin

[2] School of Economics and Management, Tianjin University of Technology and Education, Tianjin

来源：

Computer-Aided Design and Applications | 2024年 / 21卷 / S25期

关键词：

Computer-Aided Design; Deep Convolution Neural Network; Deep convolutional neural networks (DCNNs); Interactive Interface Design; Virtual Reality;

D O I：

10.14733/cadaps.2024.S25.124-140

中图分类号：

学科分类号：

摘要：

Aiming at the shortcomings of traditional CAD (computer-aided design) interfaces in intuition and efficiency, this article proposes a VR interactive interface design scheme combining AI (Artificial intelligence) algorithms. This method uses the powerful feature extraction and decision-making ability of DCNN (Deep Convolution Neural Network) to intelligently identify and optimize the user's operation in the process of CAD design and, at the same time, provide a natural and intuitive interactive way in the VR environment. The experimental results show that compared with RNN (Recurrent Neural Network) and BPNN (Back Propagation Neural Network), the DCNN proposed in this article takes 0.541 unit time to complete the task, and the error rate is lower, only 2.41%. In addition, the accuracy of the interactive interface in identifying user operations and presenting design results is over 97.01%, and the comprehensive score of the user satisfaction survey is about 95%. Therefore, this article draws a conclusion that the VR interactive interface design scheme combined with DCNN can significantly improve the efficiency and user experience of CAD design and provide a more efficient, accurate, and natural interactive way for future CAD design. This research achievement is of great significance for promoting the deep integration of CAD design and VR technology. © 2024 U-turn Press LLC,.

引用

页码：124 / 140

页数：16

共 17 条

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