  Deep learning models are the state-of-the-art methods for semantic point
cloud segmentation, the success of which relies on the availability of
large-scale annotated datasets. However, it can be extremely time-consuming and
prohibitively expensive to compile such datasets. In this work, we propose an
active learning approach to maximize model performance given limited annotation
budgets. We investigate the appropriate sample granularity for active selection
under realistic annotation cost measurement (clicks), and demonstrate that
super-point based selection allows for more efficient usage of the limited
budget compared to point-level and instance-level selection. We further exploit
local consistency constraints to boost the performance of the super-point based
approach. We evaluate our methods on two benchmarking datasets (ShapeNet and
S3DIS) and the results demonstrate that active learning is an effective
strategy to address the high annotation costs in semantic point cloud
segmentation.
