论文标题
PANOVIT：单个全景图像的视觉变压器用于房间布局的估算
PanoViT: Vision Transformer for Room Layout Estimation from a Single Panoramic Image
论文作者
论文摘要
在本文中，我们提出了Panovit，这是一种全景变压器，以估算单个全景图像的房间布局。与CNN模型相比，我们的Panovit更熟练从全景图像中学习全球信息，以估算复杂的房间布局。考虑到透视图像和等应角图像之间的差异，我们设计了一种新颖的经常性位置嵌入和一种用于处理全景图像的贴片采样方法。除了提取全局信息外，Panovit还包括一个频域边缘增强模块和3D损失，以在全景图像中提取本地几何特征。几个数据集的实验结果表明，我们的方法在房间布局预测准确性中的最先进解决方案优于最先进的解决方案。
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image. Compared to CNN models, our PanoViT is more proficient in learning global information from the panoramic image for the estimation of complex room layouts. Considering the difference between a perspective image and an equirectangular image, we design a novel recurrent position embedding and a patch sampling method for the processing of panoramic images. In addition to extracting global information, PanoViT also includes a frequency-domain edge enhancement module and a 3D loss to extract local geometric features in a panoramic image. Experimental results on several datasets demonstrate that our method outperforms state-of-the-art solutions in room layout prediction accuracy.
