text: Title: About implementation and transformations
  Body: Hi,
  
  First of all great work! But I have some questions:
  
  1) Your implementation is using Conv2D and in the first layer it has kernel size of (1,3) so it is basically taking linear combinations of the coordinates (assume no activation here). So instead of using Conv2D using Conv1D on the points and taking the x, y, z coordinates as channels should be the same right? Is there a reasong why you prefer Conv2D?
  
  2) I did not understand the underlying theory for using the transformation nets. I get the fact that the network should not be effected by the transformations but we are not learning transformations separately. Can you provide me a document so I can understand what is going on there?