The test error is large

[XiaoshuiHuang]

Hi authors, thanks for your code. I trained the model by following your instructions. The error of PointNetLK is very large. I tried a lot of times. The errors are still large. Do you know what is the problem? The errors show below:

test, 0/1202, 2.841065
test, 1/1202, 3.167926
test, 2/1202, 3.015713
test, 3/1202, 3.097108
test, 4/1202, 3.093638
test, 5/1202, 2.960435
test, 6/1202, 0.006093
test, 7/1202, 1.114216
test, 8/1202, 3.233206
test, 9/1202, 3.199666
test, 10/1202, 3.116020
test, 11/1202, 3.075513
test, 12/1202, 3.103952
test, 13/1202, 2.838798
test, 14/1202, 3.170758
test, 15/1202, 3.019938
test, 16/1202, 2.854872
test, 17/1202, 2.970612
test, 18/1202, 3.127913
test, 19/1202, 1.735299

The error of ICP shows below:
test, 9/1202, 0.011994
test, 10/1202, 0.000000
test, 11/1202, 0.633587
test, 12/1202, 0.469648
test, 12/1202, 0.139527
test, 13/1202, 0.017881
test, 13/1202, 0.264104
test, 14/1202, 0.349899
test, 14/1202, 0.226185
test, 15/1202, 0.026434
test, 15/1202, 0.039178
test, 16/1202, 0.010611
test, 17/1202, 0.000000
test, 18/1202, 0.017347
test, 16/1202, 0.010419
test, 17/1202, 0.313736
test, 19/1202, 0.060660

[XiaoshuiHuang]

Hi @prachees, I have already tried to use average pooling. Regarding the loss function, I have changed the loss function code into
return torch.nn.functional.mse_loss(r, z, reduction='sum')
The error of the results is still very large. The loss function implementation is different from the paper. I have tried to remove the loss_r and only use the loss_g as the paper said. After several days of training, the test error is still very large.

[hmgoforth]

How does the training look? Does the loss decrease as expected?