  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.
