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

Abstract

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.

References in corpus (7)

• A comprehensive survey on point cloud registration
• Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
• ME-PCN: Point Completion Conditioned on Mask Emptiness
• PointAttN: You Only Need Attention for Point Cloud Completion
• SRPCN: Structure Retrieval based Point Completion Network
• High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling
• Learning geometry-image representation for 3D point cloud generation

Cited by in corpus (7)

• Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook
• Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey
• CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
• High-throughput 3D shape completion of potato tubers on a harvester
• Shape Completion with Points in the Shadow
• FSH3D: 3D Representation via Fibonacci Spherical Harmonics
• Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net