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dc.contributor.authorDyke, R. M.en_US
dc.contributor.authorStride, C.en_US
dc.contributor.authorLai, Y.-K.en_US
dc.contributor.authorRosin, P. L.en_US
dc.contributor.authorAubry, M.en_US
dc.contributor.authorBoyarski, A.en_US
dc.contributor.authorBronstein, A. M.en_US
dc.contributor.authorBronstein, M. M.en_US
dc.contributor.authorCremers, D.en_US
dc.contributor.authorFisher, M.en_US
dc.contributor.authorGroueix, T.en_US
dc.contributor.authorGuo, D.en_US
dc.contributor.authorKim, V. G.en_US
dc.contributor.authorKimmel, R.en_US
dc.contributor.authorLähner, Z.en_US
dc.contributor.authorLi, K.en_US
dc.contributor.authorLitany, O.en_US
dc.contributor.authorRemez, T.en_US
dc.contributor.authorRodolà, E.en_US
dc.contributor.authorRussell, B. C.en_US
dc.contributor.authorSahillioglu, Y.en_US
dc.contributor.authorSlossberg, R.en_US
dc.contributor.authorTam, G. K. L.en_US
dc.contributor.authorVestner, M.en_US
dc.contributor.authorWu, Z.en_US
dc.contributor.authorYang, J.en_US
dc.contributor.editorBiasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remcoen_US
dc.date.accessioned2019-05-04T14:06:06Z
dc.date.available2019-05-04T14:06:06Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-077-2
dc.identifier.issn1997-0471
dc.identifier.urihttps://doi.org/10.2312/3dor.20191069
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20191069
dc.description.abstractThe registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset of scanned 3D shapes undergoing various controlled deformations (articulating, bending, stretching and topologically changing), along with ground truth correspondences. With the aid of this tiered benchmark of increasingly challenging real scans, we explore this problem and investigate how robust current state-of- the-art methods perform in different challenging registration and correspondence scenarios. We discover that changes in topology is a challenging problem for some methods and that machine learning-based approaches prove to be more capable of handling non-isometric deformations on shapes that are moderately similar to the training set.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.subjectComputing methodologies
dc.subjectMesh geometry models
dc.subjectShape analysis
dc.titleShape Correspondence with Isometric and Non-Isometric Deformationsen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.description.sectionheadersSHREC Session 2
dc.identifier.doi10.2312/3dor.20191069
dc.identifier.pages111-119


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