  Impressive performance on point cloud semantic segmentation has been achieved
by fully-supervised methods with large amounts of labelled data. As it is
labour-intensive to acquire large-scale point cloud data with point-wise
labels, many attempts have been made to explore learning 3D point cloud
segmentation with limited annotations. Active learning is one of the effective
strategies to achieve this purpose but is still under-explored. The most recent
methods of this kind measure the uncertainty of each pre-divided region for
manual labelling but they suffer from redundant information and require
additional efforts for region division. This paper aims at addressing this
issue by developing a hierarchical point-based active learning strategy.
Specifically, we measure the uncertainty for each point by a hierarchical
minimum margin uncertainty module which considers the contextual information at
multiple levels. Then, a feature-distance suppression strategy is designed to
select important and representative points for manual labelling. Besides, to
better exploit the unlabelled data, we build a semi-supervised segmentation
framework based on our active strategy. Extensive experiments on the S3DIS and
ScanNetV2 datasets demonstrate that the proposed framework achieves 96.5% and
100% performance of fully-supervised baseline with only 0.07% and 0.1% training
data, respectively, outperforming the state-of-the-art weakly-supervised and
active learning methods. The code will be available at
https://github.com/SmiletoE/HPAL.
