  This paper presents a new approach to recognize elements in floor plan
layouts. Besides walls and rooms, we aim to recognize diverse floor plan
elements, such as doors, windows and different types of rooms, in the floor
layouts. To this end, we model a hierarchy of floor plan elements and design a
deep multi-task neural network with two tasks: one to learn to predict
room-boundary elements, and the other to predict rooms with types. More
importantly, we formulate the room-boundary-guided attention mechanism in our
spatial contextual module to carefully take room-boundary features into account
to enhance the room-type predictions. Furthermore, we design a
cross-and-within-task weighted loss to balance the multi-label tasks and
prepare two new datasets for floor plan recognition. Experimental results
demonstrate the superiority and effectiveness of our network over the
state-of-the-art methods.
