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.

翻译：全监督方法通过大量标注数据在点云语义分割上取得了显著性能。然而，获取带有逐点标注的大规模点云数据需要大量人工成本，因此许多研究尝试探索利用有限标注进行三维点云分割学习。主动学习是实现该目标的有效策略之一，但相关研究仍不充分。现有方法主要通过对预划分区域的不确定性度量进行人工标注，但存在冗余信息问题，且需要额外进行区域划分。本文通过设计层次化点结构主动学习策略来解决该问题。具体而言，我们利用层次化最小间隔不确定性模块对每个点进行多层级上下文信息的不确定性度量，并设计特征距离抑制策略选择重要且具有代表性的点进行人工标注。此外，为更好地利用未标注数据，我们基于主动策略构建了半监督分割框架。在S3DIS和ScanNetV2数据集上的大量实验表明，本框架仅使用0.07%和0.1%的训练数据即可分别达到全监督基线模型96.5%和100%的性能，优于当前最先进的弱监督及主动学习方法。代码将发布于 https://github.com/SmiletoE/HPAL。