Your first point seems reasonable but not obvious for me, I would be convinced if model trained with augmented point clouds will perform better then one without it.
And not like we use all points in our model. For example for object detection from lidar you need a way to make their number variable, because in each iteration you will get different number of points from senior, of course you can do preprocessing, but I hope you got the point.
Usually augmentation allow you to increase sample of your input/output space that will lead to better map function that your model will learn.
I also have problem with that interpolation that OP uses is linear, but no one stopping you from modifying code yourself if necessary.
kakhaev t1_isjpe62 wrote
Reply to comment by hellrail in [P] I built densify, a data augmentation and visualization tool for point clouds by jsonathan
Your first point seems reasonable but not obvious for me, I would be convinced if model trained with augmented point clouds will perform better then one without it.
And not like we use all points in our model. For example for object detection from lidar you need a way to make their number variable, because in each iteration you will get different number of points from senior, of course you can do preprocessing, but I hope you got the point.
Usually augmentation allow you to increase sample of your input/output space that will lead to better map function that your model will learn.
I also have problem with that interpolation that OP uses is linear, but no one stopping you from modifying code yourself if necessary.