  Point cloud completion is a generation and estimation issue derived from the
partial point clouds, which plays a vital role in the applications in 3D
computer vision. The progress of deep learning (DL) has impressively improved
the capability and robustness of point cloud completion. However, the quality
of completed point clouds is still needed to be further enhanced to meet the
practical utilization. Therefore, this work aims to conduct a comprehensive
survey on various methods, including point-based, convolution-based,
graph-based, and generative model-based approaches, etc. And this survey
summarizes the comparisons among these methods to provoke further research
insights. Besides, this review sums up the commonly used datasets and
illustrates the applications of point cloud completion. Eventually, we also
discussed possible research trends in this promptly expanding field.
