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。

基于交叉尺度扭曲感知的室内全景房间布局估计中分离正交平面

Disentangling Orthogonal Planes for Indoor Panoramic Room Layout  Estimation with Cross-Scale Distortion Awareness

Multiple near frontal-parallel planes based depth representation demonstrated
impressive results in self-supervised monocular depth estimation (MDE).
Whereas, such a representation would cause the discontinuity of the ground as
it is perpendicular to the frontal-parallel planes, which is detrimental to the
identification of drivable space in autonomous driving. In this paper, we
propose the PlaneDepth, a novel orthogonal planes based presentation, including
vertical planes and ground planes. PlaneDepth estimates the depth distribution
using a Laplacian Mixture Model based on orthogonal planes for an input image.
These planes are used to synthesize a reference view to provide the
self-supervision signal. Further, we find that the widely used resizing and
cropping data augmentation breaks the orthogonality assumptions, leading to
inferior plane predictions. We address this problem by explicitly constructing
the resizing cropping transformation to rectify the predefined planes and
predicted camera pose. Moreover, we propose an augmented self-distillation loss
supervised with a bilateral occlusion mask to boost the robustness of
orthogonal planes representation for occlusions. Thanks to our orthogonal
planes representation, we can extract the ground plane in an unsupervised
manner, which is important for autonomous driving. Extensive experiments on the
KITTI dataset demonstrate the effectiveness and efficiency of our method. The
code is available at this https URL

本文提出了一种基于正交平面的深度估计方法 PlaneDepth，使自监督学习中的深度估计更为精确，同时解决了自动驾驶中分割、补洞等问题，实现了无监督地提取地面平面，为自动驾驶技术的发展提供了有效支持。