论文标题
部分可观测时空混沌系统的无模型预测
LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network
论文作者
论文摘要
单个全景图使用深层神经网络的3D房间布局估算取得了长足的进步。但是，以前的方法无法获得唯一的边界或地平线深度纬度的房间布局的有效几何意识。我们表明，使用地平线深度以及房间高度可以在水平和垂直方向上获得房间布局的全几何意识。此外，我们提出了一个平面几何意识损失函数，并具有正态的正常梯度，以监督墙壁的平稳性和转弯的转弯。我们提出了一个有效的网络LGT-NET，用于房间布局估计，其中包含一种新型的变压器体系结构，称为SWG-Transformer来建模几何关系。 SWG转换器由（移动的）窗口块和全局块组成，以结合局部和全球几何关系。此外，我们设计了变压器的新型相对位置嵌入，以增强全景的空间识别能力。实验表明，所提出的LGT-NET比基准数据集上的当前最新技术（SOTA）取得更好的性能。
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.
