Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data. Unsupervised point cloud representation learning, which aims to learn general and useful point cloud representations from unlabelled point cloud data, has recently attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using DNNs. It first describes the motivation, general pipelines as well as terminologies of the recent studies. Relevant background including widely adopted point cloud datasets and DNN architectures is then briefly presented. This is followed by an extensive discussion of existing unsupervised point cloud representation learning methods according to their technical approaches. We also quantitatively benchmark and discuss the reviewed methods over multiple widely adopted point cloud datasets. Finally, we share our humble opinion about several challenges and problems that could be pursued in future research in unsupervised point cloud representation learning. A project associated with this survey has been built at https://github.com/xiaoaoran/3d_url_survey.

翻译：点云数据因其在各种恶劣环境下卓越的精度和鲁棒性而得到广泛研究。与此同时，深度神经网络（DNNs）在监控、自动驾驶等多种应用场景中取得了极为瞩目的成功。点云与深度神经网络的融合催生了许多深度点云模型，这些模型大多在大规模、密集标注的点云数据监督下进行训练。无监督点云表示学习旨在从未标注的点云数据中学习通用且有用的点云表征，由于其能突破大规模点云标注的局限，近年来受到越来越多的关注。本文对基于深度神经网络的无监督点云表示学习进行了全面综述。文章首先阐述了相关研究的动机、通用流程及术语。随后简要介绍了广泛采用的点云数据集与深度神经网络架构等背景知识。接着，根据技术方法对现有无监督点云表示学习方法进行了深入讨论。我们还基于多个广泛使用的点云数据集，对所述方法进行了定量基准测试与讨论。最后，就无监督点云表示学习领域未来研究中可探索的若干挑战与问题，我们分享了浅见。与本综述相关的项目已在 https://github.com/xiaoaoran/3d_url_survey 构建。