In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of point cloud data in various tasks and applications. Label-efficient learning offers a promising solution by enabling effective deep network training with much-reduced annotation efforts. This paper presents the first comprehensive survey of label-efficient learning of point clouds. We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the progress achieved in this area. To achieve this, we propose a taxonomy that organizes label-efficient learning methods based on the data prerequisites provided by different types of labels. We categorize four typical label-efficient learning approaches that significantly reduce point cloud annotation efforts: data augmentation, domain transfer learning, weakly-supervised learning, and pretrained foundation models. For each approach, we outline the problem setup and provide an extensive literature review that showcases relevant progress and challenges. Finally, we share insights into current research challenges and potential future directions. A project associated with this survey has been built at \url{https://github.com/xiaoaoran/3D_label_efficient_learning}.

翻译：在过去十年中，深度神经网络在点云学习领域取得了显著进展。然而，收集大规模精确标注的训练数据极其费时费力，这不仅限制了现有点云数据集的可扩展性，也为各类任务与应用中高效挖掘点云数据造成了瓶颈。标签高效学习通过大幅减少标注工作即可实现深度网络的有效训练，为此提供了有前景的解决方案。本文首次对点云标签高效学习进行了全面综述。我们针对这一新兴研究领域的三个关键问题展开论述：i) 标签高效学习在点云处理中的重要性与紧迫性，ii) 其涵盖的子领域，以及iii) 该领域已取得的进展。为此，我们提出了一种基于不同标签类型数据先决条件来组织标签高效学习方法的分类体系。我们归纳了四类能显著降低点云标注工作量的典型标签高效学习方法：数据增强、领域迁移学习、弱监督学习及预训练基础模型。针对每类方法，我们阐述了问题设定，并提供了展示相关进展与挑战的广泛文献评述。最后，我们深入分析了当前研究面临的挑战及潜在未来方向。本综述的相关项目已发布于\url{https://github.com/xiaoaoran/3D_label_efficient_learning}。