Based on the Manhattan World assumption, most existing indoor layout
estimation schemes focus on recovering layouts from vertically compressed 1D
sequences. However, the compression procedure confuses the semantics of
different planes, yielding inferior performance with ambiguous
interpretability.
To address this issue, we propose to disentangle this 1D representation by
pre-segmenting orthogonal (vertical and horizontal) planes from a complex
scene, explicitly capturing the geometric cues for indoor layout estimation.
Considering the symmetry between the floor boundary and ceiling boundary, we
also design a soft-flipping fusion strategy to assist the pre-segmentation.
Besides, we present a feature assembling mechanism to effectively integrate
shallow and deep features with distortion distribution awareness. To compensate
for the potential errors in pre-segmentation, we further leverage triple
attention to reconstruct the disentangled sequences for better performance.
Experiments on four popular benchmarks demonstrate our superiority over
existing SoTA solutions, especially on the 3DIoU metric. The code is available
at https://github.com/zhijieshen-bjtu/DOPNet.

该研究提出了一种基于分离正交平面、软翻转融合策略、特征组装机制和三重注意力的室内布局估计方法，通过实验表明其在四个基准测试中具有优越性能。