You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
It is clear from your paper that the input to the first MLP for feature extraction is a 1D vector containing the features from the vertex's neighbors. However, I do not understand how you build a 1D vector from a set of 1 or more neighbors?
In your code, the PointSet Pooling class uses as an input a [N, M] tensor with the point features. This wouldn't be consistent with the paper. Can you please explain how this tensor 2D tensor is transformed into a 1D tensor that you use as an input for the feature extraction MLP?
Additionally, Do you use the same MLP for all sets of neighbors? Or do you use a different MLP for each set?
The text was updated successfully, but these errors were encountered:
Dear authors,
It is clear from your paper that the input to the first MLP for feature extraction is a 1D vector containing the features from the vertex's neighbors. However, I do not understand how you build a 1D vector from a set of 1 or more neighbors?
In your code, the PointSet Pooling class uses as an input a [N, M] tensor with the point features. This wouldn't be consistent with the paper. Can you please explain how this tensor 2D tensor is transformed into a 1D tensor that you use as an input for the feature extraction MLP?
Additionally, Do you use the same MLP for all sets of neighbors? Or do you use a different MLP for each set?
The text was updated successfully, but these errors were encountered: