点云
云计算
计算机科学
过程(计算)
点(几何)
集合(抽象数据类型)
数据科学
人工智能
数据挖掘
数学
几何学
操作系统
程序设计语言
作者
Nathan Brightman,Lei Fan,Yang Zhao
出处
期刊:AIMS geosciences
[American Institute of Mathematical Sciences]
日期:2023-01-01
卷期号:9 (1): 68-85
被引量:2
标识
DOI:10.3934/geosci.2023005
摘要
<abstract> <p>A point cloud is a set of data points in space. Point cloud registration is the process of aligning two or more 3D point clouds collected from different locations of the same scene. Registration enables point cloud data to be transformed into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to those reliant on targets being placed in the scene before data capture, there are various registration methods available that are based on using only the point cloud data captured. Until recently, cloud-to-cloud registration methods have generally been centered upon the use of a coarse-to-fine optimization strategy. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of deep learning methods applied to imagery data, attempts at applying these approaches to point cloud datasets have received much attention. This study reviews and comments on more recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made.</p> </abstract>
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