HyNet: A novel hybrid deep learning approach for efficient interior design texture retrieval

Abstract

Interior designers are suffering from a lack of intelligent design methods. This study aims to enhance the accuracy and efficiency of retrieval textures for interior design, which is a crucial step toward intelligent design. Currently, interior designers rely on repetitive tasks to obtain textures from websites, which is ineffective as a interior design often requires hundreds of textures. To address this issue, this study proposes a hybrid deep learning approach, HyNet, which boosts retrieval efficiency by recommending similar textures instead of blindly searching. Additionally, a new indoor texture dataset is created to support the application of artificial intelligence in this field. The results demonstrate that the proposed method’s ten recommended images achieve a high accuracy rate of 91.41%. This is a significant improvement in efficiency, which can facilitate the design industry’s progression towards intelligence. Overall, this study offers a promising solution to the challenges facing interior designers, and it has the potential to significantly enhance the industry’s productivity and innovation.

A New Approach to Interior Design: Generating Creative Interior Design Videos of Various Design Styles from Indoor Texture-Free 3D Models

“…With improved living standards, more residents aspire to personalized and exquisitely designed residences [1][2][3][4][5]. However, designers face challenges regarding insufficient creativity and cumbersome design processes when adopting traditional interior design methods [6][7][8][9][10][11]. The lack of creativity is due to the random emergence of design inspiration [6,12,13], while complex workflows decrease the design efficiency [6,14,15].…”