三维点云去噪技术  

Three-dimensional point cloud denoising

作　　者：肖俊 石光田 XIAO Jun;SHI Guangtian(School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院大学人工智能学院,北京100049

出　　处：《中国科学院大学学报（中英文）》2023年第5期577-595,共19页Journal of University of Chinese Academy of Sciences

基　　金：国家自然科学基金(U21A20515,U2003109);中国科学院战略性先导科技专项(XDA23090304)资助。

摘　　要：调研点云去噪的相关研究工作,根据相关算法的实现原理将其划分为基于优化思想的传统算法和基于深度学习思想的去噪算法,论述每类算法的研究进展,并对代表性算法进行详细分析,结合数据集、评估指标、实验结果等对其进行深入比较,在此基础上讨论当前点云去噪技术面临的问题和可能的发展方向及趋势。With the development of 3D data acquisition technology,point cloud wins the favor of researchers for it’s simple but effective representation and it is widely used in the fields of remote sensing,scene reconstruction,3D modeling,etc.Considering that the data acquisition process is easily disturbed by many factors such as equipment,environment and material,raw point cloud is often corrupted with noise and so it is of great significance to explore robust and efficient denoising algorithms.This paper firstly investigates the relevant research works of point cloud denoising and divides them into traditional algorithms based on the optimization idea and denoising algorithms based on the deep learning idea according to the implementation principles.Secondly,the research progress of each kind of algorithm is discussed and a detailed analysis of representative algorithms is presented.Thirdly,the data sets,the evaluation metrics and experimental results are summarized with an in-depth comparison.Finally,the problems and possible development directions and trends of point cloud denoising are prospected.

关 键 词：三维点云 去噪 优化思想 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]