Point-to-point correspondence reconstruction about surfmnet-pytorch

[pvnieo]

Hi @ihmc3jn09hk ,
Some datasets in the zoo are already preprocessed so the shape area is equal to one, so that's not a problem.

Concerning your question, here a way to obtain the p2p map:

from sklearn.neighbors import NearestNeighbors

neigh = NearestNeighbors(n_jobs=-1)
neigh.fit(np.matmul(evecs_x, C1.transpose()))
p2p = neigh.kneighbors(evecs_y, 1, return_distance=False).flatten()

I'm using sklearn because it's faster, but you can use numpy as well. Hope this answer your question.

[ihmc3jn09hk]

Hi @ihmc3jn09hk ,
Some datasets in the zoo are already preprocessed so the shape area is equal to one, so that's not a problem.

Concerning your question, here a way to obtain the p2p map:

from sklearn.neighbors import NearestNeighbors

neigh = NearestNeighbors(n_jobs=-1)
neigh.fit(np.matmul(evecs_x, C1.transpose()))
p2p = neigh.kneighbors(evecs_y, 1, return_distance=False).flatten()

I'm using sklearn because it's faster, but you can use numpy as well. Hope this answer your question.

Thanks @pvnieo , I have tried the suggested one with some modifications as following to resolve some error. But the out p2p is identical to the one I had before. I think the problem is pin-pointed to my trained-weights.
It would be kind if you can share what parameters you used for training and a result sample overfit weight file for me to test my machine is broken ?

Shame, this part is my implementation problem.
With the weights, the output p2p for all 1500 matches is just a single point in shape X.

Finally got it working for the tiny set. The code works in training mode while I was using evaluation for the whole time.
The training curves are for k=60 and k=120.