Learning Spherical Convolution for Fast Features from 360 Imagery

While 360 cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield "flat" filters, yet 360 images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360 imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360 data, sensitive to the varying distortion effects across the viewing sphere.