Existing panoramic layout estimation solutions tend to recover room
boundaries from a vertically compressed sequence, yielding imprecise results as
the compression process often muddles the semantics between various planes.
Besides, these data-driven approaches impose an urgent demand for massive data
annotations, which are laborious and time-consuming. For the first problem, we
propose an orthogonal plane disentanglement network (termed DOPNet) to
distinguish ambiguous semantics. DOPNet consists of three modules that are
integrated to deliver distortion-free, semantics-clean, and detail-sharp
disentangled representations, which benefit the subsequent layout recovery. For
the second problem, we present an unsupervised adaptation technique tailored
for horizon-depth and ratio representations. Concretely, we introduce an
optimization strategy for decision-level layout analysis and a 1D cost volume
construction method for feature-level multi-view aggregation, both of which are
designed to fully exploit the geometric consistency across multiple
perspectives. The optimizer provides a reliable set of pseudo-labels for
network training, while the 1D cost volume enriches each view with
comprehensive scene information derived from other perspectives. Extensive
experiments demonstrate that our solution outperforms other SoTA models on both
monocular layout estimation and multi-view layout estimation tasks.

通过提出正交平面解缠网络 (DOPNet) 以分辨模糊语义和针对水平深度与比例的无监督自适应技术，本研究在全景布局估计和多视角布局估计任务中优于其他 SoTA 模型。