Persistent Homology and the Discrete Laplace Operator For Mesh Similarity

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dc.contributor.authorHajij, Mustafaen_US
dc.contributor.authorZhang, Yunhaoen_US
dc.contributor.authorLiu, Haowenen_US
dc.contributor.authorRosen, Paulen_US
dc.contributor.editorRitsos, Panagiotis D. and Xu, Kaien_US
dc.date.accessioned2020-09-10T06:27:51Z
dc.date.available2020-09-10T06:27:51Z
dc.date.issued2020
dc.description.abstractWe use persistent homology along with the eigenfunctions of the Laplacian to study similarity amongst geometric and combinatorial objects. Our method relies on studying the lower-star filtration induced by the eigenfunctions of the Laplacian. This gives us a shape descriptor that inherits the rich information encoded in the eigenfunctions of the Laplacian. Moreover, the similarity between these descriptors can be easily computed using tools that are readily available in Topological Data Analysis. We provide experiments to illustrate the effectiveness of the proposed method.en_US
dc.description.sectionheadersGraphics
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20201153
dc.identifier.isbn978-3-03868-122-9
dc.identifier.pages67-70
dc.identifier.urihttps://doi.org/10.2312/cgvc.20201153
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20201153
dc.publisherThe Eurographics Associationen_US
dc.titlePersistent Homology and the Discrete Laplace Operator For Mesh Similarityen_US
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Computer Graphics & Visual Computing (CGVC) 2020