Floor Plan Restoration: A Multimodal Method Under One Second

Floor plan restoration aims to recover vector and semantic information from raster floor plan images, which is significant for advanced applications including interior design, interative walkthroughs, and layout planning. Existing methods generally adopt a two-stage paradigm: a parsing stage to extract semantic elements such as rooms, walls, doors, and windows from raster images; and then a vectorization stage to convert them into vector graphics. However, these methods are deficient in both accuracy and efficiency due to the neglect of the unique cues of floor plans compared to natural images. To address the above issues, we propose MMParseNet that yields accurate parsing results by incorporating multimodal cues unique to floor plans, such as room names, furniture icons, and room boundaries. Moreover, we implement an efficiency-optimized vectorization method based on PCA that avoids unnecessary iterative solutions. We conduct both quantitative and qualitative experiments on three public and one self-built dataset. The results exhibit consistent improvements in accuracy and sub-second overall restoration time across various datasets.