Indoor panorama typically consists of human-made structures parallel or
perpendicular to gravity. We leverage this phenomenon to approximate the scene
in a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this
end, we propose an effective divide-and-conquer strategy that divides pixels
based on their plane orientation estimation; then, the succeeding instance
segmentation module conquers the task of planes clustering more easily in each
plane orientation group. Besides, parameters of V-planes depend on camera yaw
rotation, but translation-invariant CNNs are less aware of the yaw change. We
thus propose a yaw-invariant V-planar reparameterization for CNNs to learn. We
create a benchmark for indoor panorama planar reconstruction by extending
existing 360 depth datasets with ground truth H\&V-planes (referred to as
PanoH&V dataset) and adopt state-of-the-art planar reconstruction methods to
predict H\&V-planes as our baselines. Our method outperforms the baselines by a
large margin on the proposed dataset.

本文提出了一种基于水平面和垂直面的 360 度室内全景图像近似场景的方法，并使用具有地面真实数据的数据集进行了测试，结果表明本文的方法在室内全景图像处理中具有更好的性能。

室内全景平面三维重建：分治算法

Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

We present HoHoNet, a versatile and efficient framework for holistic
understanding of an indoor 360-degree panorama using a Latent Horizontal
Feature (LHFeat). The compact LHFeat flattens the features along the vertical
direction and has shown success in modeling per-column modality for room layout
reconstruction. HoHoNet advances in two important aspects. First, the deep
architecture is redesigned to run faster with improved accuracy. Second, we
propose a novel horizon-to-dense module, which relaxes the per-column output
shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoNet is
fast: It runs at 52 FPS and 110 FPS with ResNet-50 and ResNet-34 backbones
respectively, for modeling dense modalities from a high-resolution $512 \times
1024$ panorama. HoHoNet is also accurate. On the tasks of layout estimation and
semantic segmentation, HoHoNet achieves results on par with current
state-of-the-art. On dense depth estimation, HoHoNet outperforms all the prior
arts by a large margin.

HoHoNet 是一个用于室内 360 度全景图的多功能高效框架，它使用潜在的水平特征（LHFeat）来实现全局理解和重建房间布局，也提出了一个新颖的 Horizon-to-Dense 模块来实现 LHFeat 的每像素密集预测，可以实现在高分辨率 512x1024 全景图中实现 52-110 FPS 的运行速度，并在布局估计、语义分割和密集深度估计任务上取得了与最先进水平相当的结果。