Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design

As the aesthetic appreciation for art continues to grow, there is an increased demand for precision and detailed control in sculptural works. The advent of 3D laser scanning technology introduces transformative new tools and methodologies for refining correction systems in sculpture design. This art...

Full description

Bibliographic Details
Published in:PeerJ Computer Science
Main Author: Zheng X.; Weng Z.
Format: Article
Language:English
Published: PeerJ Inc. 2025
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216348724&doi=10.7717%2fPEERJ-CS.2628&partnerID=40&md5=ca08419ed5dfb3580b1e345074c80b89
id 2-s2.0-85216348724
spelling 2-s2.0-85216348724
Zheng X.; Weng Z.
Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
2025
PeerJ Computer Science
11

10.7717/PEERJ-CS.2628
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216348724&doi=10.7717%2fPEERJ-CS.2628&partnerID=40&md5=ca08419ed5dfb3580b1e345074c80b89
As the aesthetic appreciation for art continues to grow, there is an increased demand for precision and detailed control in sculptural works. The advent of 3D laser scanning technology introduces transformative new tools and methodologies for refining correction systems in sculpture design. This article proposes a feature point matching algorithm based on fragment measurement and the iterative closest point (ICP) methodology, leveraging 3D laser scanning technology, namely Fragment Measurement Iterative Closest Point Feature Point Matching (FM-ICP-FPM). The FM-ICP-FPM approach uses the overlapping area of the two sculpture perspectives as a reference for attaching feature points. It employs the 3D measurement system to capture physical point cloud data from the two surfaces to enable the initial alignment of feature points. Feature vectors are generated by segmenting the region around the feature points and computing the intra-block gradient histogram. Subsequently, distance threshold conditions are set based on the constructed feature vectors and the preliminary feature point matches established during the coarse alignment to achieve precise feature point matching. Experimental results demonstrate the exceptional performance of the FM-ICP-FPM algorithm, achieving a sampling interval of 200. The correct matching rate reaches an impressive 100%, while the mean translation error (MTE) is a mere 154 mm, and the mean rotation angle error (MRAE) is 0.065 degrees. The indicator represents the degree of deviation in translation and rotation of the registered model, respectively. These low error values demonstrate that the FM-ICP-FPM algorithm excels in registration accuracy and can generate highly consistent three-dimensional models. Copyright 2025 Zheng and Weng Distributed under Creative Commons CC-BY 4.0
PeerJ Inc.
23765992
English
Article

author Zheng X.; Weng Z.
spellingShingle Zheng X.; Weng Z.
Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
author_facet Zheng X.; Weng Z.
author_sort Zheng X.; Weng Z.
title Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
title_short Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
title_full Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
title_fullStr Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
title_full_unstemmed Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
title_sort Design of an enhanced feature point matching algorithm utilizing 3D laser scanning technology for sculpture design
publishDate 2025
container_title PeerJ Computer Science
container_volume 11
container_issue
doi_str_mv 10.7717/PEERJ-CS.2628
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216348724&doi=10.7717%2fPEERJ-CS.2628&partnerID=40&md5=ca08419ed5dfb3580b1e345074c80b89
description As the aesthetic appreciation for art continues to grow, there is an increased demand for precision and detailed control in sculptural works. The advent of 3D laser scanning technology introduces transformative new tools and methodologies for refining correction systems in sculpture design. This article proposes a feature point matching algorithm based on fragment measurement and the iterative closest point (ICP) methodology, leveraging 3D laser scanning technology, namely Fragment Measurement Iterative Closest Point Feature Point Matching (FM-ICP-FPM). The FM-ICP-FPM approach uses the overlapping area of the two sculpture perspectives as a reference for attaching feature points. It employs the 3D measurement system to capture physical point cloud data from the two surfaces to enable the initial alignment of feature points. Feature vectors are generated by segmenting the region around the feature points and computing the intra-block gradient histogram. Subsequently, distance threshold conditions are set based on the constructed feature vectors and the preliminary feature point matches established during the coarse alignment to achieve precise feature point matching. Experimental results demonstrate the exceptional performance of the FM-ICP-FPM algorithm, achieving a sampling interval of 200. The correct matching rate reaches an impressive 100%, while the mean translation error (MTE) is a mere 154 mm, and the mean rotation angle error (MRAE) is 0.065 degrees. The indicator represents the degree of deviation in translation and rotation of the registered model, respectively. These low error values demonstrate that the FM-ICP-FPM algorithm excels in registration accuracy and can generate highly consistent three-dimensional models. Copyright 2025 Zheng and Weng Distributed under Creative Commons CC-BY 4.0
publisher PeerJ Inc.
issn 23765992
language English
format Article
accesstype
record_format scopus
collection Scopus
_version_ 1825722576142336000