Intelligent Analysis and Optimization of Computer Aided Furniture Design by Deep Learning

Abstract

Furniture design is an indispensable part of interior design; furniture design not only needs to meet the needs of consumers for basic functions but also to meet the aesthetic needs of consumers' personalized design. Traditional furniture design is centered on the designer. Furniture design is completed based on market demand, but the personalized needs of consumers are ignored, which easily produces the problem of homogenization of design. Therefore, this paper builds an intelligent furniture design analysis and optimization model based on deep learning, combines CNN and KNN to recognize and classify furniture design styles, and optimizes furniture design through the Pix2pix model. The experimental results show that the model has good and stable performance in furniture design style recognition and classification. Design optimization can be realized based on designer design results combined with consumer feedback information, and the efficiency is faster. In addition, the furniture design output of the model, according to the design labels provided by designers and consumers, can meet the needs of most consumers for personalized and comfortable furniture design.