  We study the problem of estimating room layouts from a single panorama image.
Most former works have two stages: feature extraction and parametric model
fitting. Here we propose an end-to-end method that directly predicts parametric
layouts from an input panorama image. It exploits an implicit encoding
procedure that embeds parametric layouts into a latent space. Then learning a
mapping from images to this latent space makes end-to-end room layout
estimation possible. However end-to-end methods have several notorious
drawbacks despite many intriguing properties. A widely raised criticism is that
they are troubled with dataset bias and do not transfer to unfamiliar domains.
Our study echos this common belief. To this end, we propose to use semantic
boundary prediction maps as an intermediate domain. It brings significant
performance boost on four benchmarks (Structured3D, PanoContext, S3DIS, and
Matterport3D), notably in the zero-shot transfer setting. Code, data, and
models will be released.
