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
this https URL

通过开发一种分层点云主动学习策略，本文解决了使用有限注释学习 3D 点云分割的问题，通过使用上下文信息的分层最小边距不确定性模块对每个点进行不确定性度量，并设计了特征距离抑制策略来选择重要和代表性的点进行手动标注，在 S3DIS 和 ScanNetV2 数据集上的广泛实验表明，所提出的框架在仅使用 0.07% 和 0.1% 的训练数据时分别达到了完全监督基线的 96.5% 和 100% 的性能，优于最先进的弱监督和主动学习方法。