The Impact of the Number of k-Means Clusters on 3D Point Cloud Registration

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dc.contributor.authorAnkomah, Peter
dc.contributor.authorVangorp, Peter
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-24T19:09:48Z
dc.date.available2024-07-24T19:09:48Z
dc.date.issued2024
dc.description.abstract-translatedPoint cloud registration plays a crucial role in many applications, from robotics and autonomous navigation to medical imaging and 3D scene reconstruction. While the Iterative Closest Point (ICP) algorithm is a well-known shape registration choice, its efficiency and accuracy can be affected by the vast search space for point correspon dences. k-means clustering emerges as a promising solution for partitioning the search space into smaller clusters to reduce the computational complexity and increase the performance of the matching. However, the number and size of these clusters and how they affect the registration remains a critical and yet not fully explored factor. This paper delves into the relationship between the number of k-means clusters and point cloud registration accuracy. To determine the effect of the number of k-means clusters on registration accuracy and efficiency and to understand any emerging pattern, k-meansICP is developed to use the k-means algorithm to cluster the correspondence search space. Two sets of 3D molecular shapes with differing complexities are matched using initial rotation angles 15, 30, and 60 degrees with 2 to 10 k-means clusters. The results are then compared with the original ICP algorithmen
dc.format2 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 3-4.en
dc.identifier.doihttps://doi.org/10.24132/CSRN.3401.2
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57372
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subject3D bodový mrakcs
dc.subjectregistrace 3D tvarucs
dc.subjectiterativní nejbližší bodcs
dc.subjectk-Means Clusteringcs
dc.subject.translated\cs_CZ
dc.subject.translated3D Point Clouden
dc.subject.translated3D Shape Registrationen
dc.subject.translatediterative closest pointen
dc.subject.translatedk-Means Clusteringen
dc.titleThe Impact of the Number of k-Means Clusters on 3D Point Cloud Registrationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen

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