A GPU Based High-efficient and Accurate Optimal Pose Alignment Approach of 3D Objects

dc.contributor.authorZhang, Qianen_US
dc.contributor.authorJia, Jinyuanen_US
dc.contributor.editorH. Laga and T. Schreck and A. Ferreira and A. Godil and I. Pratikakis and R. Veltkampen_US
dc.date.accessioned2013-04-25T14:10:28Z
dc.date.available2013-04-25T14:10:28Z
dc.date.issued2011en_US
dc.description.abstractIn this paper we present a new method for alignment of 3D objects. This approach is based on the exhaustive optimization search in the 3D space using GPU based genetic algorithm. The descriptor of 3D object used as the objective function to be optimized is a newly developed pose-variant similarity measure, which is obtained directly from the voxelized model's geometry and could be entirely implemented on the GPU. In order to reduce the traditional optimal algorithms' large processing time, we exploit the GPU's highly parallel architecture and transport our approach from CPU to GPU. Experimental results show that the proposed method is superior to existing normalization techniques such as PCA and provides a high degree of precision to align 3D objects.en_US
dc.description.sectionheadersShort Papersen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-31-6en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR11/097-100en_US
dc.publisherThe Eurographics Associationen_US
dc.titleA GPU Based High-efficient and Accurate Optimal Pose Alignment Approach of 3D Objectsen_US
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