Hugues THOMAS

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Both strategies would work indeed

> print(len(training_dataset[0])) what is `training_dataset` in this case? If it is the one defined in the training script, you should not use training_dataset[i] to access data this is not the...

for the first bug, it seems the code does not see any deformable convolution in the network. For the second bug, I cannot help much as it is not a...

The config for different datasets is mostly the same, only some data related parameters should change. There are numbers of issues where I already talked about config parameters. You can...

The point features are in the outputs of the network: https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/73e444d486cd6cb56122c3dd410e51c734064cfe/utils/trainer.py#L188 You can see them used in the validation function: https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/73e444d486cd6cb56122c3dd410e51c734064cfe/utils/trainer.py#L480-L484

Seems you are providing an empty `truth` array in the confusion matrix function. You can try to track what happens by following the code backwards. The truth arrays come from...

Hi @tengyu-liu, In `tester.py`, follow the execution of the code in the `test_cloud_segmentation` function. On the dataset side, what you want to follow are the functions `get_batch_gen` and `get_tf_mapping`. The...

Hi @nikogamulin, Thank you for your interest in my work. This problem of yours seems very interesting and hard to tackle. First I think that the success or failure of...

Hi @Violetit, Sorry for the late reply, `spatially_regular_gen()` function is a python generator so you should be able to use a simple python print to see the values in there....