Application Analysis of Particle Swarm Optimization Convolutional Neural Network in Industrial Design

As an important design resource, the quantity of computer-aided design (CAD) models has increased dramatically with the popularization of 3D CAD technology. In order to solve the problem of 3D reconstruction of CAD model and make it serve for industrial design and modeling, this article studies the application of deep learning (DL) algorithm and computer aided industrial design (CAID) in industrial design, and proposes a 3D reconstruction and rendering model based on particle swarm optimization convolutional neural network (PSO-CNN). This model uses mutual attention mechanism to establish long-distance correlation between source domain and target domain, and uses attention-driven modeling, so that the source domain image can directly learn the key features in the target domain. On this basis, for large-size images, the mutual attention mechanism is further improved to a multi-head mutual attention mechanism to save more computer memory costs. The simulation results show that the model can not only reconstruct the 3D structure of an object based on a single-view image, but also render the 3D structure of the object, giving full play to the advantages of many samples and wide types of image data and the powerful representation ability of DL, realizing the 3D reconstruction of an object based on a single-view image and rendering the reconstructed 3D model. © 2024, CAD Solutions, LLC. All rights reserved.