  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.
