Different range for sampling coordinates and network predictions about ndf HOT 2 OPEN

@lykius
thanks!
to your points:

• Yes, exactly - for the grid_sample function we need to convert to [-1,1] , additional 0.5 serves as slack - not strongly necessary theoretically.

• Points in [-1,1] in grid sample coordiantes correspond to [-0.5,0.5] in mesh coordinates, so there is no mismatch.

• The movements are predicted in mesh space, the grid_sample space is just intermediate and only important for implementation. The network is supervised with mesh space and the results are interpreted in mesh space - so this is correct as is.

• We are sampling random points in a symmetric cube - so we don't need to swap any coordinates.

Best
Julian

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Hi @jchibane,

I think @lykius is right here. The code here should be samples = samples - F.normalize(gradient, dim=2) * df_pred.reshape(-1, 1)*2. Because your inputs are grid coordinate points [-1,1] and your outputs are udf under mesh space [-0.5,0.5]. Here the samples are grid coordinate points instead of mesh space points. You move the grid coordinates points instead of mesh space points here.

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