  Inherent ambiguity in layout annotations poses significant challenges to
developing accurate 360{\deg} room layout estimation models. To address this
issue, we propose a novel Bi-Layout model capable of predicting two distinct
layout types. One stops at ambiguous regions, while the other extends to
encompass all visible areas. Our model employs two global context embeddings,
where each embedding is designed to capture specific contextual information for
each layout type. With our novel feature guidance module, the image feature
retrieves relevant context from these embeddings, generating layout-aware
features for precise bi-layout predictions. A unique property of our Bi-Layout
model is its ability to inherently detect ambiguous regions by comparing the
two predictions. To circumvent the need for manual correction of ambiguous
annotations during testing, we also introduce a new metric for disambiguating
ground truth layouts. Our method demonstrates superior performance on benchmark
datasets, notably outperforming leading approaches. Specifically, on the
MatterportLayout dataset, it improves 3DIoU from 81.70% to 82.57% across the
full test set and notably from 54.80% to 59.97% in subsets with significant
ambiguity. Project page: https://liagm.github.io/Bi_Layout/
