Research On Deep Learning Algorithms For 3D Content Generation Based On Scene Graph Understanding

Computer-aided industrial design has brought fundamental changes to the modern industrial design model.Through digital and visual modeling processes,the design efficiency and scientificity have been greatly improved.Computer-aided industrial design is gradually developing towards intelligence.The application of artificial intelligence technology in various fields provides a reference for the intelligentization of industrial design.Industrial design puts forward requirements for the efficiency and creativity of design.The ability of learning and reasoning in artificial intelligence technology can provide support for the intelligentization of industrial design.This article proposes a deep learning algorithm for intelligently synthesizing the appearance of 3D objects based on artificial intelligence technology.It aims to solve the problem of intelligent generation of 3D appearance of products in industrial design.The main contents are as follows:This article first explains the theoretical basis of the 3D deep learning algorithm,discusses the general composition of the neural network from the aspects of general architecture,basic components,and optimization.Finally,it analyzes the deep learning architecture of the 3D vision task,focusing on the deep learning algorithm 3D generation task.The feasibility of providing a theoretical basis for the needs of 3D intelligent modeling.Based on 3D modeling theory and deep learning principles,this paper proposes a 3D industrial product appearance modeling algorithm based on generative adversarial networks.Generative adversarial network is the most widely used architecture in the generation task.In this paper,a deep learning algorithm suitable for 3D intelligent modeling is customized for this architecture,and the algorithm is evaluated from multiple perspectives such as generation efficiency and generation effect.Finally,an engineering application framework for intelligent industrial design is proposed around the algorithm,and verification is performed for 3D printing tasks.Based on the analysis of the 3D industrial product appearance modeling algorithm based on the generative confrontation network,it is found that its generation efficiency and local detail performance can be improved.This paper proposes an industrial product 3D appearance modeling algorithm based on scene graph understanding.For the purpose of better adapting to auxiliary industrial design,this paper proposes a 3D generation algorithm based on the understanding of scene graphs.The idea of coarse to fine and distributed generation is introduced into the algorithm process,and the composition relationship of 3D objects is combined with graph neural network.Learn and understand,and complete the generation of 3D objects from a finer granularity.The deep learning algorithm and related solutions proposed in this study provide a reference for the intelligent generation of 3D product shape in the field of computer-aided industrial design,and have high practical application value.