I’m currently attempting to reimplement at first, a 2d version of Pu-net, but I’ve given up on that and went straight to a “cut-down” Pointnet Version. From the PU-net paper:

Hence, we
further add another convolution (with separate weights) for
each feature set. Since we train the network to learn the r
different convolutions for the r feature sets, these new features can include more diverse information, thus reducing
their correlations.

In the implementation, they seemed to be not adding a relu activation functions, so I tried that, but that didn’t seem to have the desired effect.