3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG Transformer to model geometry relations. SWG Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets.

利用视域深度和房间高度可以在水平和垂直方向上获得全向几何感知，提出了一种平面几何感知损失函数，使用局部和全局几何关系的SWF-Transformer建立几何关系模型，以及相对位​​置嵌入增强空间识别能力，证明LGT-Net在基准数据集上比当前最先进技术表现更好。

LGT-Net：基于几何感知变换网络的室内全景房间布局估计