Abstract: Pixel-level floor plan space recognition plays an important role in applications such as floor plan review and model reconstruction from drawings. Targeting at housing floor plans, the existing methods recognize spaces directly based on semantic segmentation. Public architectural floor plans feature more noising lines and elements, higher resolution, and more space varieties. Higher resolution makes it hard to acquire global information in a floor plan, while the variety of spaces makes it impossible to gain the clear range of room types, both features rendering the existing space recognition approaches unpractical. To recognize spaces in public architectural floor plans, a dataset named Public Architectural Floor Plan Dataset was proposed, including 20 floor plans labeled with walls at the pixel level and 100 floor plans labeled with elements at the bounding box level. A deep learning-based space boundary recognition approach was proposed. This approach could enhance the accuracy in recognizing walls, with the proposed center line extraction and key line minimum square error loss function, and could recognize spaces by enclosing space. A space contour optimization algorithm was proposed, which in experiments could reduce the number of contour points and reserve the shape of spaces. Experimental results show that this method breaks through the limitation of resolution and room type range, attains satisfying space recognition performance, and presents a solution to recognizing spaces of public architectural floor plans. Compared with existing methods, the proposed method reaches a higher recall ratio while the precision score is guaranteed.

Key words: computer vision, floor plan recognition, public architectural floor plan, pixel-level floor plan, wall
recognition, space recognition