TACO: a Benchmark for Connectivity-invariance in Shape Correspondence

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dc.contributor.authorPedico, Simoneen_US
dc.contributor.authorMelzi, Simoneen_US
dc.contributor.authorMaggioli, Filippoen_US
dc.contributor.editorCaputo, Arielen_US
dc.contributor.editorGarro, Valeriaen_US
dc.contributor.editorGiachetti, Andreaen_US
dc.contributor.editorCastellani, Umbertoen_US
dc.contributor.editorDulecha, Tinsae Gebrechristosen_US
dc.date.accessioned2024-11-11T12:48:29Z
dc.date.available2024-11-11T12:48:29Z
dc.date.issued2024
dc.description.abstractIn real-world scenarios, a major limitation for shape-matching datasets is represented by having all the meshes of the same subject share their connectivity across different poses. Specifically, similar connectivities could provide a significant bias for shape matching algorithms, simplifying the matching process and potentially leading to correspondences based on the recurring triangle patterns rather than geometric correspondences between mesh parts. As a consequence, the resulting correspondence may be meaningless, and the evaluation of the algorithm may be misled. To overcome this limitation, we introduce TACO, a new dataset where meshes representing the same subject in different poses do not share the same connectivity, and we compute new ground truth correspondences between shapes. We extensively evaluate our dataset to ensure that ground truth isometries are properly preserved. We also use our dataset for validating state-of-the-art shape-matching algorithms, verifying a degradation in performance when the connectivity gets altered.en_US
dc.description.sectionheadersShape Analysis
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20241344
dc.identifier.isbn978-3-03868-265-3
dc.identifier.issn2617-4855
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20241344
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20241344
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
dc.subjectComputing methodologies → Shape analysis
dc.subjectTheory of computation → Computational geometry
dc.titleTACO: a Benchmark for Connectivity-invariance in Shape Correspondenceen_US
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Italian Chapter Conference 2024 - Smart Tools and Apps in Graphics