Spectral-based Segmentation for Functional Shape-matching

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dc.contributor.authorMancinelli, Claudioen_US
dc.contributor.authorMelzi, Simoneen_US
dc.contributor.editorBanterle, Francescoen_US
dc.contributor.editorCaggianese, Giuseppeen_US
dc.contributor.editorCapece, Nicolaen_US
dc.contributor.editorErra, Ugoen_US
dc.contributor.editorLupinetti, Katiaen_US
dc.contributor.editorManfredi, Gildaen_US
dc.date.accessioned2023-11-12T15:37:36Z
dc.date.available2023-11-12T15:37:36Z
dc.date.issued2023
dc.description.abstractIn Computer Graphics and Computer Vision, shape co-segmentation and shape-matching are fundamental tasks with diverse applications, from statistical shape analysis to human-robot interaction. These problems respectively target establishing segmentto- segment and point-to-point correspondences between shapes, which are crucial task for numerous practical scenarios. Notably, co-segmentation can aid in point-wise correspondence estimation in shape-matching pipelines like the functional maps framework. Our paper introduces an innovative shape segmentation pipeline which provides coherent segmentation for shapes within the same class. Through comprehensive evaluation on a diverse test set comprising shapes from various datasets and classes, we demonstrate the coherence of our segmentation approach. Moreover, our method significantly improves accuracy in shape matching scenarios, as evidenced by comparisons with the original functional maps approach. Importantly, these enhancements come with minimal computational overhead. Our work not only introduces a novel coherent segmentation method and a valuable tool for improving correspondence accuracy within functional maps, but also contributes to the theoretical foundations of this impactful field, inspiring further research.en_US
dc.description.sectionheadersGeometry processing
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20231294
dc.identifier.isbn978-3-03868-235-6
dc.identifier.issn2617-4855
dc.identifier.pages47-58
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20231294
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20231294
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Shape analysis; Theory of computation -> Computational geometry; Mathematics of computing -> Functional analysis
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.subjectMathematics of computing
dc.subjectFunctional analysis
dc.titleSpectral-based Segmentation for Functional Shape-matchingen_US
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