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.

本研究提出了一种主动学习方法，并通过限制注释费用来确定适当的样本粒度进行主动选择，结果表明基于超点的样本选择比点级和实例级选择使用有限预算更有效，并进一步利用本地一致性约束来提高超点选择方法的性能，在 ShapeNet 和 S3DIS 两个基准数据集上评估了我们的方法，结果表明主动学习是解决语义点云分割中高注释成本的有效策略。