  Recent approaches for predicting layouts from 360 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 [3], and introduce two
depth-based evaluation metrics.
