BIM product recommendation for intelligent design using style learning

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Building information modeling (BIM) has been widely adopted in architectural interior design due to its characteristics such as visualization, coordination and simulation. Currently, an increasing number of BIM products are created to facilitate BIM interior design, which triggers new demand for BIM product recommendations to improve design efficiency by directly recommending satisfactory BIM products for designers. Current efforts mainly focus on the text-based BIM product retrieval and ignore the high-level semantic concept of style features. Although style consistency is one of the critical design principles, BIM product recommendation satisfying style consistency remains to be unexplored. This study proposes a novel design recommendation scheme for BIM products using style learning, termed DrStyle. Firstly, a product representative image selection model is designed to identify a representative image retaining the best style of a BIM product. Then, the DrStyle employs a style learning algorithm to extract the style feature vector using the representative image as input. Finally, a BIM product recommendation scheme is proposed to recommend BIM products by computing the similarities of style feature vectors. The performance of the proposed DrStyle was evaluated, and the experiment results showed that the DrStyle achieved an average precision of 68.8% purely using the style. This study complements intelligent design from a novel perspective of style consistency and will inspire more comprehensive intelligent design schemes.