Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 12; pp. 22862 - 22883
• Main Authors: Fei, Ben, Yang, Weidong, Chen, Wen-Ming, Li, Zhijun, Li, Yikang, Ma, Tao, Hu, Xing, Ma, Lipeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D vision; Cloud computing; completion; Computer vision; Deep learning; Laser radar; point cloud; Point cloud compression; Shape; Solid modeling; Task analysis; Three dimensional models; Three-dimensional displays
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3195555

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications of 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, view-based, convolution-based, graph-based, generative model-based, transformer-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.