  Given a single RGB panorama, the goal of 3D layout reconstruction is to
estimate the room layout by predicting the corners, floor boundary, and ceiling
boundary. A common approach has been to use standard convolutional networks to
predict the corners and boundaries, followed by post-processing to generate the
3D layout. However, the space-varying distortions in panoramic images are not
compatible with the translational equivariance property of standard
convolutions, thus degrading performance. Instead, we propose to use spherical
convolutions. The resulting network, which we call OmniLayout performs
convolutions directly on the sphere surface, sampling according to inverse
equirectangular projection and hence invariant to equirectangular distortions.
Using a new evaluation metric, we show that our network reduces the error in
the heavily distorted regions (near the poles) by approx 25 % when compared to
standard convolutional networks. Experimental results show that OmniLayout
outperforms the state-of-the-art by approx 4% on two different benchmark
datasets (PanoContext and Stanford 2D-3D). Code is available at
https://github.com/rshivansh/OmniLayout.
