pvnieo / fmnet-pytorch Goto Github PK
View Code? Open in Web Editor NEWA Pytorch implementation of: "Deep Functional Maps: Structured Prediction for Dense Shape Correspondence"
License: MIT License
A Pytorch implementation of: "Deep Functional Maps: Structured Prediction for Dense Shape Correspondence"
License: MIT License
Hi, @pvnieo,
The link "https://github.com/pvnieo/datasets-zoo" for the dataset is broken. Could you fix it or point me to the download repo?
Thanks~
Hi @pvnieo , thank you for sharing the implementation. As suggested, FAUST dataset is used with preprocess.py. The program just stopped and reported 13x GiB memory is required to be allocated. Is there any way to reduce the amount required?
HI, @pvnieo ,
According to the faust data provided in this package, xxx.off and xxx.vts are used for experiment. As each of the correspondence file (xxx.vts) is related to the template shape, do we need the template shape file for visualizing and evaluating the point-to-point map? And where could we download the template shape file for faust data?
Thanks~
Hi @pvnieo , thank you for the update of the repo.
I am not sure whether it is a mistake on the code or my understanding.
Should the loss involve the difference to shape_x's geodesics distance ?
e.g.
loss = torch.sqrt(((P * geodesic_dist_y - geodesic_dist_x ) ** 2).sum((1, 2)))
In the updated code, the calculation does not involve any information towards shape_x.
Line 23 in 116b48e
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