Number of points in data_a and data_b in demo

[weijiawang96]

Dear Mattia,

Sorry to bother you, but I'm having difficulties in understanding the demo.sh code. I guess demo.sh is to test our trained networks with our test dataset? Please correct me if I'm wrong.

The main part I don't understand is line 509 in trainer.py:
data_a = EasyDict(self.datasets.dataset_test[self.classes[0]][index_a]) (same for data_b)

I think data_a['points'] should be the normalised and downsampled shape, I can see it has 2500 points. However, I ran train.sh and demo.sh with --number_points = 642 and --decoder_type = 'atlasnet'. I really don't know why in demo.sh, each loaded sample has 2500 points but not 642 points.

Thank you in advance. Looking forward to your help!

Sincerely,
Wei

[csjxchen]

The bottlenecks "meshflow" and "atlasnet" both deform a set of sampled points (sampled from sphere and square) and the number of generated points depends on the number of sampled points rather the design of model.
The number of generated points (--number_points) can be alterable in training and inference.