  This paper presents a new approach for the recognition of elements in floor
plan layouts. Besides of elements with common shapes, we aim to recognize
elements with irregular shapes such as circular rooms and inclined walls.
Furthermore, the reduction of noise in the semantic segmentation of the floor
plan is on demand. To this end, we propose direction-aware, learnable, additive
kernels in the application of both the context module and common convolutional
blocks. We apply them for high performance of elements with both common and
irregular shapes. Besides, an adversarial network with two discriminators is
proposed to further improve the accuracy of the elements and to reduce the
noise of the semantic segmentation. Experimental results demonstrate the
superiority and effectiveness of the proposed network over the state-of-the-art
methods.

Computer Vision and Pattern Recognition;Machine Learning