Article(id=1309475178791399740, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475167726830266, articleNumber=null, orderNo=null, doi=10.19710/J.cnki.1003-8817.20250178, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790132811495, onlineDateStr=2026-09-23, pubDate=1779206400000, pubDateStr=2026-05-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790132811495, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790132811495, creator=13701087609, updateTime=1790132811495, updator=13701087609, issue=Issue{id=1309475167726830266, tenantId=1146029695717560320, journalId=1189873562199433220, year='2026', volume='', issue='5', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1779206400000', pubDateStr='2026-05-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1790132808857, creator='13701087609', updateTime=1790132999714, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309475968293634075, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475167726830266, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309475968293634076, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475167726830266, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=9, endPage=14, ext={EN=ArticleExt(id=1309475178980143421, articleId=1309475178791399740, tenantId=1146029695717560320, journalId=1189873562199433220, language=EN, title=Hole Detection Method for Automotive Component Point Clouds Based on α-Shapes and Feature Constraints, columnId=null, journalTitle=Automobile Technology & Material, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of low accuracy and poor robustness in hole detection within 3D scanned point clouds of automotive components, this paper proposes a hole detection method that integrates α-Shapes with geometric-topological dual constraints. First, an improved voxel filter is used to preprocess the point cloud for noise reduction; Subsequently, a multi-scale triangular mesh is constructed via α-Shapes, and potential hole regions are extracted by combining boundary analysis; Finally, curvature features and normal vector consistency constraints are introduced to screen for real holes.Experimental results show that on a dataset containing 30 groups of automotive component point clouds, the proposed method achieves a hole detection accuracy of 97.3%. Its recognition rate and efficiency are both improved compared with the traditional Delaunay algorithm, and it can effectively adapt to scenarios with complex surfaces and multi-scale holes.

, authors=Tian Gu, Chengwen Wang, Yang Wang, Mingwei Tang, Shengxi Jin, Zheng Sun, authorsList=Tian Gu, Chengwen Wang, Yang Wang, Mingwei Tang, Shengxi Jin, Zheng Sun, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309475179441516862, articleId=1309475178791399740, tenantId=1146029695717560320, journalId=1189873562199433220, language=CN, title=基于α-Shapes与特征约束的汽车零部件点云孔洞检测方法, columnId=1190284220841955836, journalTitle=汽车工艺与材料, columnName=生产现场, runingTitle=null, highlight=null, articleAbstract=

为解决汽车零部件三维扫描点云中孔洞检测精度低、鲁棒性差的问题，提出一种融合α-Shapes与几何-拓扑双约束的孔洞检测方法。首先，利用改进的体素滤波对点云进行降噪预处理；随后通过α-Shapes构建多尺度三角网格，结合边界分析提取潜在孔洞区域；最后，引入曲率特征与法向量一致性约束筛选真实孔洞。验证结果表明，在包含30组汽车零部件点云的数据集上，所提出方法的孔洞检测准确率达到97.3%，识别率和效率相较于传统德洛内（Delaunay）算法均有一定提升，并能有效适应复杂曲面与多尺度孔洞场景。

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古田（1986—），男，工程师，硕士学位，研究方向为汽车尺寸工程数字化。

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古田（1986—），男，工程师，硕士学位，研究方向为汽车尺寸工程数字化。

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