Should calibration be re-computed for inference on new data?

[floriandeboissieu]

Hi Thomas Hugues, many thanks for this pytorch version of KPConv.

Applying a trained model on new data with different point density using test_model.py recomputes the calibration if the batch_limits.pkl and neighbor_limits.pkl are not found in the data directory. I am not sure if that is what is expected, especially for neighbor_limits.

Would a different the neighbor limit on the first layer have an influence on the result ?

Or should I keep them (at least the neighbor limits, i.e. neighbor_limits.pkl) the same as for training?

After reading carefully the code and the issues I wasn't able to have a clear answer about that.