No, why should it.

This densification can make it easier to reach a generalizing training state, but the generalized state probably performs worse than a well generalized state without the augmentation as it changes the distribution to learn slightly by artificially imposing that a portion of the points are the center of mass of a triangulation of another portion of points. That is not generally the case for sensor data that will come in, therefore the modified distribution has low relevance to the real distribution that one wants to learn.