Optimization design of interior space based on the two-stage deep learning network and Single sample-driven method

Abstract

This study addresses the limitations of traditional interior space design, particularly the timeliness and uniqueness of solutions, by proposing an optimized design framework that integrates a two-stage deep learning network with a single-sample-driven mechanism. In the first stage, the framework employs a Transformer network to extract multi-dimensional features (such as spatial layout, color distribution, furniture style, etc.) from input space images, generating an initial feature vector. In the second stage, a diffusion model is introduced to iteratively optimize the design results based on user-provided single-sample features. The model’s performance is validated using multiple publicly available datasets, including InteriorNet, SUN RGB-D, NYU Depth V2, and ScanNet. Experimental results demonstrate that, compared to traditional methods, the design cycle is reduced by 40%, space utilization is increased by 25%, and proportional and scale coordination is improved by 20%. The single-sample-driven personalized design strategy results in a 30% significant improvement in color matching scores. Through the synergistic effect of feature extraction and generative optimization, the two-stage network enhances both design efficiency and the innovativeness and user adaptability of the solution. This study not only offers an efficient and intelligent solution for interior space design but also presents a new technological paradigm for the advancement of artificial intelligence-driven design fields.