Interior Design Evaluation Based on Deep Learning: A Multi-Modal Fusion Evaluation Mechanism

Yiyan Fan, Yang Zhou () and Zheng Yuan
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Yiyan Fan: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China
Yang Zhou: School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China
Zheng Yuan: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China

Mathematics, 2024, vol. 12, issue 10, 1-15

Abstract: The design of 3D scenes is of great significance, and one of the crucial areas is interior scene design. This study not only pertains to the living environment of individuals but also has applications in the design and development of virtual environments. Previous work on indoor scenes has focused on understanding and editing existing indoor scenes, such as scene reconstruction, segmentation tasks, texture, object localization, and rendering. In this study, we propose a novel task in the realm of indoor scene comprehension, amalgamating interior design principles with professional evaluation criteria: 3D indoor scene design assessment. Furthermore, we propose an approach using a transformer encoder–decoder architecture and a dual-graph convolutional network. Our approach facilitates users in posing text-based inquiries; accepts input in two modalities, point cloud representations of indoor scenes and textual queries; and ultimately generates a probability distribution indicating positive, neutral, and negative assessments of interior design. The proposed method uses separately pre-trained modules, including a 3D visual question-answering module and a dual-graph convolutional network for identifying emotional tendencies of text.

Keywords: interior design; 3D question answering; dual-graph convolutional networks; sentiment analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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