  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}.

Computer Vision and Pattern Recognition