论文标题
Hohonet：360室内整体理解，具有潜在的水平特征
HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
论文作者
论文摘要
我们提出了Hohonet，这是一种使用潜在的水平特征（LHFEAT）的多功能且有效的框架，用于对室内360度全景的整体理解。紧凑的LHFEAT使沿垂直方向的特征平移，并在为房间布局重建的每柱模态建模方面取得了成功。 Hohonet在两个重要方面进步。首先，重新设计了深度架构，以提高精度运行速度。其次，我们提出了一个新型的地平线到密度的模块，该模块放松了每柱输出形状的约束，从而可以从LHFEAT中进行单像素密集的预测。 Hohonet快速：它以52 fps和110 fps的速度运行，分别具有Resnet-50和Resnet-34骨架，用于对高分辨率$ 512 \ times 1024 $ Panorama的密集模式进行建模。 Hohonet也是准确的。关于布局估计和语义细分的任务，Hohonet与当前的最新成绩相当。在密集的深度估计上，霍诺内特的表现要优于所有先前的艺术。
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.
