Self-Supervised Learning of 3D Point Clouds: A Survey

The use of 3D point cloud is widespread in robotics and autonomous driving systems. With the development of deep learning, an increasing number of models are being proposed to address tasks in 3D point cloud processing, including shape classification and 3D object detection. However, training these models requires large amounts of labeled data, which is expensive to obtain. Therefore, self-supervised learning methods for 3D point clouds have recently gained significant attention, which train the model with unlabeled data by designing pretext tasks. This paper aims to review these methods. Based on the pretext tasks designed, we divide those more than 30 existing methods into three categories: reconstruction-based, contrastive-based and MAE(Masked Autoencoder)-based methods. Then, we introduce the research motivations, implementations and characteristics of these methods one by one. Finally, two performance evaluation criteria are introduced and the performance of each self-supervised learning method is assessed and analyzed based on these criteria.