Persistent Heat Signature for Pose-oblivious Matching of Incomplete Models

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dc.contributor.authorTamal K. Deyen_US
dc.contributor.authorKuiyu Lien_US
dc.contributor.authorChuanjiang Luoen_US
dc.contributor.authorPawas Ranjanen_US
dc.contributor.authorIssam Safaen_US
dc.contributor.authorYusu Wangen_US
dc.date.accessioned2015-02-23T17:15:32Z
dc.date.available2015-02-23T17:15:32Z
dc.date.issued2010en_US
dc.description.abstractAlthough understanding of shape features in the context of shape matching and retrieval has made considerable progress in recent years, the case for partial and incomplete models in presence of pose variations still begs a robust and efficient solution. A signature that encodes features at multi-scales in a pose invariant manner is more appropriate for this case. The Heat Kernel Signature function from spectral theory exhibits this multi-scale property. We show how this concept can be merged with the persistent homology to design a novel efficient poseoblivious matching algorithm for all models, be they partial, incomplete, or complete. We make the algorithm scalable so that it can handle large data sets. Several test results show the robustness of our approach.en_US
dc.description.number5en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume29en_US
dc.identifier.doi10.1111/j.1467-8659.2010.01763.xen_US
dc.identifier.pages1545-1554en_US
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/CGF.v29i5pp1545-1554en_US
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/CGF.v29i5pp1545-1554
dc.titlePersistent Heat Signature for Pose-oblivious Matching of Incomplete Modelsen_US
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29-Issue 5
SGP10: Eurographics Symposium on Geometry Processing