A BRIEF OVERVIEW OF THE CURRENT STATE, CHALLENGING ISSUES AND FUTURE DIRECTIONS OF POINT CLOUD REGISTRATION

N. Brightman, L. Fan*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Point cloud registration is the process of transforming multiple point clouds obtained at different locations of the same scene into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to the typical target-based registration method, there are various registration methods that are based on using only the point cloud data captured (i.e. cloud-to-cloud methods). Until recently, cloud-to-cloud registration methods have generally adopted a coarse-to-fine optimisation process. 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 applying deep learning approaches to imagery data, numerous attempts at applying such approaches to point cloud datasets have received much attention. This study reviews and comment on 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.

Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Pages17-23
Number of pages7
DOIs
Publication statusPublished - 27 Oct 2022
Event14th GeoInformation for Disaster Management, Gi4DM 2022 - Beijing, China
Duration: 1 Nov 20224 Nov 2022

Conference

Conference14th GeoInformation for Disaster Management, Gi4DM 2022
Country/TerritoryChina
CityBeijing
Period1/11/224/11/22

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Laser Scanning
  • LiDAR
  • Point Cloud
  • Registration

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