In this work, we address the semantic segmentation of various types of 2D floorplans. Previous works mainly focus on the segmentation of furnished floorplans and are not competent at segmenting floorplans with bare walls and very few semantic symbols and furniture, e.g., typical finishing floorplans, because these methods usually neglect important structural information, such as walls and doors, for the floorplan segmentation as shown in Fig. 1. On the contrary, interior designers determine the semantics of different rooms based on structural information, e.g., the straight walls and curved doors, for further refurbishing and furnishing design. Based on this observation, we propose a structural line primitive-based framework to tackle this problem by incorporating line contexts and mutual line relations with a transformer-based network. Besides, we further collect an interior finishing floorplan dataset with more diverse semantic labels for evaluation. When applied to the proposed dataset, the method outperforms existing approaches by a large margin (+5.33% mIoU, +4.10% mAcc). Experiments on furnished floorplan segmentation datasets showcase that the proposed method also outperforms previous counterparts with 80.48(+2.95)% mIoU, 89.48(+1.68)% mAcc on R2V, 89.12(+4.92)% mIoU, and 93.87(+2.56)% mAcc on CubiCasa5k. The project will be released.
