论文标题
方向感知，可学习，添加剂内核和深层平面图识别的对抗性网络
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
论文作者
论文摘要
本文提出了一种新方法，以识别平面图布局中的元素。除了具有常见形状的元素外，我们还旨在识别具有不规则形状的元素，例如圆形房间和倾斜的墙壁。此外，按需，在平面图的语义分割中降低噪声。为此，我们在应用上下文模块和常见卷积块的应用中提出了方向感知，可学习的，添加剂的内核。我们将它们应用于具有常见和不规则形状的元素的高性能。此外，提出了一个具有两个歧视因子的对抗网络，以进一步提高元素的准确性并减少语义分割的噪声。实验结果证明了所提出的网络比最先进的方法具有优势和有效性。
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.
