Get depth, regions, and layout from panoramic image quickly and accurately with horizontal features
Get depth, regions, and layout from panoramic image quickly and accurately with horizontal features
HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
arXiv paper abstract https://arxiv.org/abs/2011.11498
arXiv PDF paper https://arxiv.org/pdf/2011.11498.pdf
YouTube (5 min) https://www.youtube.com/watch?v=xXtRaRKmMpA
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.
... 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 512x1024 panorama.
... 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.
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