Recent approaches for predicting layouts from 360\(^{\circ }\) panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a post-processing step by fitting a 3D layout to the layout elements. Until now, it has been difficult to compare the methods due to multiple different design decisions, such as the encoding network (e.g., SegNet or ResNet), type of elements predicted (e.g., corners, wall/floor boundaries, or semantic segmentation), or method of fitting the 3D layout. To address this challenge, we summarize and describe the common framework, the variants, and the impact of the design decisions. For a complete evaluation, we also propose extended annotations for the Matterport3D dataset (Chang et al.: Matterport3d: learning from rgb-d data in indoor environments. arXiv:1709.06158, 2017), and introduce two depth-based evaluation metrics.

从360 \（^ {\ circ} \）预测布局的最新方法全景效果极佳。这些方法建立在一个由三个步骤组成的通用框架上：一个基于基于边缘的对齐的预处理步骤，一个布局元素的预测以及一个将3D布局拟合到布局元素的后处理步骤。到目前为止，由于多种不同的设计决策（例如编码网络（例如，SegNet或ResNet），预测的元素类型（例如，边角，墙/地板边界或语义分割）），很难比较这些方法。或适合3D布局的方法。为了应对这一挑战，我们总结并描述了通用框架，变体和设计决策的影响。为了进行完整的评估，我们还建议对Matterport3D数据集进行扩展注释（Chang等人：Matterport3d：从室内环境中的rgb-d数据中学习。