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.
翻译:点云的完成是部分点云产生的一代和估计问题,在3D计算机愿景的应用中发挥着关键作用。深入学习(DL)的进展极大地提高了点云完成的能力和稳健性。然而,完成点云的质量仍需进一步提高,以达到实际利用,因此,这项工作的目的是对各种方法进行全面调查,包括点基、革命基、图表基和基因化模型方法等。本调查总结了这些方法之间的比较,以激发进一步的研究洞察力。此外,本审查总结了常用的数据集,并说明了点云完成的应用。最后,我们还讨论了这个迅速扩大的领域可能的研究趋势。