@ Usually augmentation allow you to increase sample of your input/output space that will lead to better map function that your model will learn.

More data better results in general yes, but if the additional data is worthless, its a bit scam. That will be recognized in a comparison with an equally well trained state without that augmentation (might be harder to reach) tested on relevant data.

Technically put: the learned distribution is altered to a surrogate pointcloud which is quite similar to the relevant distribution of sensor data that will be produced measuring the real world, but is not the same anymore. Thats the price for more training data with this, and i wouldnt pay it because my primary goal is to capture the relevant distribution as Close as possible.