基于深度学习的成对点云刚性配准现状与进展  

Status and progress of deep learning-based pairwise point cloud rigid registration

作　　者：周汝琴 王鹏[1] 戴晨光[1] 汪汉云 江万寿[2] 张永生[1] ZHOU Ruqin;WANG Peng;DAI Chenguang;WANG Hanyun;JIANG Wanshou;ZHANG Yongsheng(Institute of Geospatial Information,Information Engineering University,Zhengzhou 450001,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China)

机构地区：[1]信息工程大学地理空间信息学院,郑州450001 [2]武汉大学测绘遥感信息工程国家重点实验室,武汉430079

出　　处：《遥感学报》2024年第12期3074-3093,共20页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42271457);武汉大学测绘遥感信息工程国家重点实验室开放基金(编号:22E03)。

摘　　要：点云刚性配准作为三维点云数据处理的一项基础任务,在自动驾驶、机器人、测绘遥感、医疗、工业设计以及文物保护等方面具有广泛应用。基于深度学习的点云配准方法能够自动学习高判别力的点云特征,取得较高的配准精度。为对点云配准进行更深入有效的探索,本文对基于深度学习的成对点云刚性配准相关技术研究进行了系统的综述与分析。首先介绍了基于深度学习的局部特征提取方法;其次,介绍对应关系解算、姿态回归和场景流估计等3类基于深度学习的配准方法相关研究进展;再次,对现有的可用于点云刚性配准的公开数据集进行了总结与归纳;最后,对点云配准研究现状进行了总结,并对该领域未来研究方向进行展望。Point cloud registration is the spatial alignment of two or more point clouds through geometric transformations.As a fundamental task in 3D point cloud data processing,point cloud registration is an important preprocessing operation for tasks,such as 3D modeling,object recognition,and scene understanding.Given the nonstructural,sparse,and uneven characteristics of point cloud data,point cloud registration remains one of the hotspots and challenges in computer vision,mapping,and remote sensing,although many studies have been conducted on it.With the emergence and rapid development of neural networks,deep learning demonstrates huge utilization potential in applications,such as point cloud classification,recognition,detection,and reconstruction.In recent years,many researchers have attempted to apply deep learning techniques to point cloud registration.Deep learning-based point cloud registration methods can automatically learn highly discriminative and robust point cloud features that contain geometric structural and semantic information,which are crucial for achieving high registration accuracy.In this study,the research on pairwise point cloud rigid registration technology based on deep learning is systematically reviewed and analyzed.First,feature extraction networks based on deep learning are introduced.Second,the progress of research on registration methods,namely,correspondence estimation,pose regression,and scene flow estimation,is reviewed,and the characteristics,advantages,and disadvantages of the three methods are summarized.Third,this study systematically summarizes and categorizes existing publicly available datasets that can be used for rigid point cloud registration.Last,the status of current research on point cloud registration is determined;the advantages and limitations of existing methods in terms of feature learning,registration accuracy,registration efficiency,and other aspects are explained;and future research directions are proposed.Specifically,three exploration directions are presented.(1)Ex

关 键 词：遥感 三维点云 刚性配准 深度学习 点云特征 对应关系解算 姿态回归 场景流估计 配准数据集 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术] P2[自动化与计算机技术—计算机科学与技术]