  Significant geometric structures can be compactly described by global
wireframes in the estimation of 3D room layout from a single panoramic image.
Based on this observation, we present an alternative approach to estimate the
walls in 3D space by modeling long-range geometric patterns in a learnable
Hough Transform block. We transform the image feature from a cubemap tile to
the Hough space of a Manhattan world and directly map the feature to the
geometric output. The convolutional layers not only learn the local
gradient-like line features, but also utilize the global information to
successfully predict occluded walls with a simple network structure. Unlike
most previous work, the predictions are performed individually on each cubemap
tile, and then assembled to get the layout estimation. Experimental results
show that we achieve comparable results with recent state-of-the-art in
prediction accuracy and performance. Code is available at
https://github.com/Starrah/DMH-Net.
