Non-rigid 3D Shape Retrieval via Sparse Representation

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dc.contributor.authorWan, Lilien_US
dc.contributor.authorLi, Shuaien_US
dc.contributor.authorMiao, Zhenjiang J.en_US
dc.contributor.authorCen, Yigang G.en_US
dc.contributor.editorBruno Levy and Xin Tong and KangKang Yinen_US
dc.date.accessioned2014-01-27T18:18:11Z
dc.date.available2014-01-27T18:18:11Z
dc.date.issued2013en_US
dc.description.abstractShape descriptor design is an important but challenging problem for non-rigid 3D shape retrieval. Recently, bagof- words based methods are widely used to integrate a model's local shape descriptors into a global histogram. In this paper, we present a new method to pool the local shape descriptors into a global shape descriptor by means of sparse representation. Firstly, we employ heat kernel signature (HKS) to depict the multi-scale local shape. Then, for each model in the training dataset, we take the HKSs corresponding to its mesh vertices to serve as training signals, and thus an over-complete dictionary can be learned from them. Finally, the HKSs of each 3D model are sparsely coded based on the learned dictionary, and such sparse representations can be further integrated to form an object-level shape descriptor. Moreover, we conduct extensive experiments on the state-of-the-art benchmarks, wherein comprehensive evaluations state our method can achieve better performance than other bag-of-words based approaches.en_US
dc.description.seriesinformationPacific Graphics Short Papersen_US
dc.identifier.isbn978-3-905674-50-7en_US
dc.identifier.urihttps://doi.org/10.2312/PE.PG.PG2013short.011-016en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectCurveen_US
dc.subjectsurfaceen_US
dc.subjectsoliden_US
dc.subjectand object representationsen_US
dc.titleNon-rigid 3D Shape Retrieval via Sparse Representationen_US
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