Parametric curve reconstruction from point clouds using minimization techniques

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dc.contributor.authorRuiz, O.E.
dc.contributor.authorCortés, C.
dc.contributor.authorAristizábal, M.
dc.contributor.authorAcosta, D.A.
dc.contributor.authorVanegas, C.A.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Procesosspa
dc.contributor.researchgroupDesarrollo y Diseño de Procesosspa
dc.date.accessioned2021-04-12T19:08:53Z
dc.date.available2021-04-12T19:08:53Z
dc.date.issued2013-01-01
dc.description.abstractCurve reconstruction from noisy point samples is central to surface reconstruction and therefore to reverse engineering, medical imaging, etc. Although Piecewise Linear (PL) curve reconstruction plays an important role, smooth (C1-, C2-,?) curves are needed for many applications. In reconstruction of parametric curves from noisy point samples there remain unsolved issues such as (1) high computational expenses, (2) presence of artifacts and outlier curls, (3) erratic behavior of self-intersecting curves, and (4) erratic excursions at sharp corners. Some of these issues are related to non-Nyquist (i.e. sparse) samples. In response to these shortcomings, this article reports the minimization-based fitting of parametric curves for noisy point clouds. Our approach features: (a) Principal Component Analysis (PCA) pre-processing to obtain a topologically correct approximation of the sampled curve. (b) Numerical, instead of algebraic, calculation of roots in point-to-curve distances. (c) Penalties for curve excursions by using point cloud to - curve and curve to point cloud. (d) Objective functions which are economic to minimize. The implemented algorithms successfully deal with self - intersecting and / or non-Nyquist samples. Ongoing research includes self-tuning of the algorithms and decimation of the point cloud and the control polygon.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=2388
dc.identifier.isbn9789898565464
dc.identifier.otherSCOPUS;2-s2.0-84878152042
dc.identifier.urihttp://hdl.handle.net/10784/28296
dc.language.isoeng
dc.relationSCOPUS;2-s2.0-84878152042
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84878152042&partnerID=40&md5=f7b1780809af1487d723ac2543ab9171
dc.sourceParametric Curve Reconstruction From Point Clouds Using Minimization Techniques
dc.subject.keywordComputational expenseeng
dc.subject.keywordControl polygonseng
dc.subject.keywordCurve reconstructioneng
dc.subject.keywordMinimization techniqueseng
dc.subject.keywordNoisy pointeng
dc.subject.keywordObjective functionseng
dc.subject.keywordParametric curveeng
dc.subject.keywordSelf-intersecting curveseng
dc.subject.keywordAlgorithmseng
dc.subject.keywordComputer graphicseng
dc.subject.keywordInformation analysiseng
dc.subject.keywordInformation systemseng
dc.subject.keywordMedical imagingeng
dc.subject.keywordOptimizationeng
dc.subject.keywordPiecewise linear techniqueseng
dc.subject.keywordPrincipal component analysiseng
dc.subject.keywordReverse engineeringeng
dc.subject.keywordCurve fittingeng
dc.titleParametric curve reconstruction from point clouds using minimization techniqueseng
dc.typeinfo:eu-repo/semantics/conferencePapereng
dc.typeconferencePapereng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
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