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点集配准综述:从两两配准到组配准。

A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration.

机构信息

Key Laboratory of Industrial IoT and Networked Control, Ministry of Education, and the Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Faculty of Science and Technology, University of Macau, Macau 999078, China.

出版信息

Sensors (Basel). 2019 Mar 8;19(5):1191. doi: 10.3390/s19051191.

Abstract

This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.

摘要

本文对点集配准技术进行了全面的文献综述。讨论并总结了点集配准的最新建模方法和算法。特别关注了用于点对配准和点集配准的方法。选择了一些最杰出的代表性方法进行定性和定量实验。通过对 2D 和 3D 数据的实验,CPD-GL 点对配准算法和 JRMPC 点集配准算法在准确性和计算复杂性方面似乎优于其竞争对手。此外,还确定了该领域的未来研究方向和途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4700/6427196/14744c768607/sensors-19-01191-g001.jpg

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