Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis release_e2kvryolufearetp4ujlw2gwwy

by Ben Fei, Weidong Yang, Wenming Chen, Zhijun Li, Yikang Li, Tao Ma, Xing Hu, Lipeng Ma

Released as a article .

2022  

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.
In text/plain format

Archived Files and Locations

application/pdf  3.6 MB
file_5yyovzxrfjdrveentfpmjey3xy
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-03-09
Version   v2
Language   en ?
arXiv  2203.03311v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: cbad4bb4-5f5c-47e0-96f2-b4ea75ba27f9
API URL: JSON