jonkhler
S2Convolution requires square inputs
Why does the S2Convolution layer require square inputs? Is there a way around this to explore the use of your convolutions on equirectangular images?
https://www.cs.dartmouth.edu/~geelong/sphere/
Here is a nice picture on how grid points lying on sphere. The dimension (2 * b_in, 2 * b_in) of the input image is the design of the grid points on the sphere. There are total 2 * b_in * 2 * b_in black dots in the picture.
Such grid point design is the convention of the discrete spherical harmonics transform used in this work.
Ah, okay. So processing an equirectangular projection would require a resampling of the image?
Equirectangular images are usually (180, 360) resolution--essentially unwrapping the sphere around the vertical axis. So I guess I'd need to resample it so that the latitudinal dimension is upsampled by a factor of 2?
Why not? In the MINST example, the projection of the 2D image to spherical image can be considered as a kind of resampling. The projection/resampling done in the MNIST example is not the only way. We should feel free to try different projection/resampling schemes to see which works better.
I thought that the MNIST dataset is projected on the northern hemisphere only, in which case having a square image makes sense. But once one works on the full sphere, then we need to resample since the spherical projection are 180° - 360° as @meder411 said.
