  Active Learning (AL) has remained relatively unexplored for LiDAR perception
tasks in autonomous driving datasets. In this study we evaluate Bayesian active
learning methods applied to the task of dataset distillation or core subset
selection (subset with near equivalent performance as full dataset). We also
study the effect of application of data augmentation (DA) within Bayesian AL
based dataset distillation. We perform these experiments on the full
Semantic-KITTI dataset. We extend our study over our existing work only on
1/4th of the same dataset. Addition of DA and BALD have a negative impact over
the labeling efficiency and thus the capacity to distill datasets. We
demonstrate key issues in designing a functional AL framework and finally
conclude with a review of challenges in real world active learning.
