Corpus ID: 227746659

Rotation-Invariant Autoencoders for Signals on Spheres

  title={Rotation-Invariant Autoencoders for Signals on Spheres},
  author={Suhas Lohit and Shubhendu Trivedi},
Omnidirectional images and spherical representations of $3D$ shapes cannot be processed with conventional 2D convolutional neural networks (CNNs) as the unwrapping leads to large distortion. Using fast implementations of spherical and $SO(3)$ convolutions, researchers have recently developed deep learning methods better suited for classifying spherical images. These newly proposed convolutional layers naturally extend the notion of convolution to functions on the unit sphere $S^2$ and the group… Expand

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