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I would like to thank you for this quite complete and easy to use implementation of the original paper
I am posting this issue to ask some questions about this library. Has there been advances on the learning part of the library ? I was interested in it and eventually adapting if I manage to get past the cython code which I'm not familiar with
At the moment, I am toying a bit with the library, my goal being to apply it on 3D points clouds. I managed to use the generic non 2D case for it, which also raised some interrogations on my part.
First, I see this is not possible to add a standard deviation parameter to a DenseCRF object when adding pairwiseenergy. However, from the formula
it seems that we can circumvent this by dividing directly the features before setting a pairwise energy this way, is that right ?
d=dcrf.DenseCRF(100, 5) # npoints, nlabelsstd=8feats=np.array(...) # Get the pairwise features from somewhere.print(feats.shape) # -> (7, 100) = (feature dimensionality, npoints)print(feats.dtype) # -> dtype('float32')dcrf.addPairwiseEnergy(feats/8)
Moreover, it seems you don't apply the 2* factor on the pairwise energy in the 2D case. while it is just a constant factor, is it intended ? I don't see traces of any power being applied either. Here is the corresponding code in DenseCRF2D::addPairwiseGaussian:
With respect to the compatibility parameter, I noted that using a constant as a value for compat and an identity matrix with the same constant gives different results. Could you confirm that a PottsCompatibility and a identity MatrixCompatibility should bring the same results ? The problem seem to come from a sign, one consider the entry should be negative, the other one positive. Putting a compatibility value such as PottsCompatibility = - MatrixCompatibility indeed gives the same result
I would be glad to exchange with you more in depth if you are still active on the library.
Thanks
The text was updated successfully, but these errors were encountered:
Hello @lucasb-eyer
I would like to thank you for this quite complete and easy to use implementation of the original paper
I am posting this issue to ask some questions about this library. Has there been advances on the learning part of the library ? I was interested in it and eventually adapting if I manage to get past the cython code which I'm not familiar with
At the moment, I am toying a bit with the library, my goal being to apply it on 3D points clouds. I managed to use the generic non 2D case for it, which also raised some interrogations on my part.
First, I see this is not possible to add a standard deviation parameter to a DenseCRF object when adding pairwiseenergy. However, from the formula
it seems that we can circumvent this by dividing directly the features before setting a pairwise energy this way, is that right ?
Moreover, it seems you don't apply the 2* factor on the pairwise energy in the 2D case. while it is just a constant factor, is it intended ? I don't see traces of any power being applied either. Here is the corresponding code in
DenseCRF2D::addPairwiseGaussian:
https://github.com/lucasb-eyer/pydensecrf/blob/0d53acbcf5123d4c88040fe68fbb9805fc5b2fb9/pydensecrf/densecrf/src/densecrf.cpp
With respect to the compatibility parameter, I noted that using a constant as a value for compat and an identity matrix with the same constant gives different results. Could you confirm that a
PottsCompatibility
and a identityMatrixCompatibility
should bring the same results ? The problem seem to come from a sign, one consider the entry should be negative, the other one positive. Putting a compatibility value such asPottsCompatibility
=- MatrixCompatibility
indeed gives the same resultI would be glad to exchange with you more in depth if you are still active on the library.
Thanks
The text was updated successfully, but these errors were encountered: