Open dituu opened 1 year ago
Thanks for your reply! I have tested the public PU-GAN and PU-GCN model on my prepared test set (random downsampling). The results are as follows, CD || HD || P2F PU-GAN 0.28 4.60 3.18 PU-GCN 0.30 4.31 2.78 It seems the CD errors are consistent with the results of possion sampling, while HD and P2F are degenerated.
Hi, thanks for releasing the code. I have a question about the test setting on PU-GAN dataset.
In original PU-GAN dataset, the test input sparse patches are generated by random downsampling form the ground truth, while this work uses Poisson downsampling in prepare_pugan.py. The former generates a realistic and non-uniform distribution, while the Poisson downsampling produces a uniform distribution.
I directly test the released model on PU-GAN test set under random downsampling setting, and find the performance degenerates greatly (CD 0.245->0.495). It seems that the method is sensitive to the input distribution pattern.
So, do you have some ideas about this phenomenon? Many thanks!