Comments (3)
Thanks for pointing out this issue. Actually it should be masking the "empty nodes" to be zero, instead of choosing point 0. I have push a bug fix to this issue. Thank you again for your advice.
from so-net.
I try to reproduce your model, but I am disappointed. Even I run your code, you can't produce 93.4 every time. I run your code three times, only one time can reach 93.4. And it's only one time in 300 epcoh???
from so-net.
There are several reasons for that result. First, there isn't validation set for ModelNet, and the testing set is kind of small. Second, the point clouds are sampled on-the-fly from objects. Any method that work on sampled point clouds suffers from performance fluctuation. The performance can be higher or lower.
from so-net.
Related Issues (20)
- How to test the model on SHREC2016 retrieval challenge? HOT 4
- Compile ‘index_max’ fail. HOT 12
- Sampling code non-uniform? HOT 4
- Retrieval result unclear HOT 3
- Question about point cloud transformation HOT 2
- index_max builds but fails at runtime HOT 1
- Error in SO-NET code HOT 12
- Supplementary material HOT 2
- How about the Training Time?
- Data augmentation not work? HOT 1
- The function of index_max. HOT 4
- How to train the model on SHREC2016
- Very low mIoU on part set HOT 1
- The error in ‘Application - Part Segmentation’ HOT 2
- How to get the input of the autoencoder?
- Low validation mIoU despite high validation accuracy on part seg HOT 1
- test file for autoencoder HOT 1
- Error in "python setup.py install" HOT 1
- can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
- how to install the index_max? HOT 1
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from so-net.