PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs. (arXiv:2104.03584v2 [cs.CV] UPDATED)

Spherical signals exist in many applications, e.g., planetary data, LiDAR
scans and digitalization of 3D objects, calling for models that can process
spherical data effectively. It does not perform well when simply projecting
spherical data into the 2D plane and then using planar convolution neural
networks (CNNs), because of the distortion from projection and ineffective
translation equivariance. Actually, good principles of designing spherical CNNs
are avoiding distortions and converting the shift equivariance property in
planar CNNs to rotation equivariance in …

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