Deep-learning-based point cloud completion methods: A review

Abstract

Point cloud completion aims to utilize algorithms to repair missing parts in 3D data for high-quality point clouds. This technology is crucial for applications such as autonomous driving and urban planning. With deep learning’s progress, the robustness and accuracy of point cloud completion have improved significantly. However, the quality of completed point clouds requires further enhancement to satisfy practical requirements. In this study, we conducted an extensive survey of point cloud completion methods, with the following main objectives: (i) We classified point cloud completion methods into categories based on their principles, such as point-based, convolution-based, GAN-based, and geometry-based methods, and thoroughly investigated the advantages and limitations of each category. (ii) We collected publicly available datasets for point cloud completion algorithms and conducted experimental comparisons using various typical deep-learning networks to draw conclusions. (iii) With our research in this paper, we discuss future research trends in this rapidly evolving field.