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dc.contributor.authorMartinek, M.en_US
dc.contributor.authorGrosso, R.en_US
dc.contributor.authorGreiner, G.en_US
dc.date.accessioned2015-02-23T09:47:10Z
dc.date.available2015-02-23T09:47:10Z
dc.date.issued2010en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/j.1467-8659.2010.01717.xen_US
dc.description.abstractThe ability to extract spatial features from 3D objects is essential for applications such as shape matching and object classification. However, designing an effective feature vector which is invariant with respect to rotation, translation and scaling is a challenging task and is often solved by normalization techniques such as PCA, which can give rise to poor object alignment. In this paper, we introduce a novel method to extract robust and invariant 3D features based on rotational symmetry. By applying a rotation-variant similarity function on two instances of the same 3D object, we can define an autocorrelation on the object in the space of rotations. We use a special representation of the SO(3) and determine significant rotation axes for an object by means of optimization techniques. By sampling the similarity function via rotations around these axes, we obtain robust and invariant features, which are descriptive for the underlying geometry. The resulting feature vector cannot only be used to characterize an object with respect to rotational symmetry but also to define a distance between 3D models. Because the features are compact and pre-computable, our method is suitable to perform similarity searches in large 3D databases.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleA Shape Descriptor for 3D Objects Based on Rotational Symmetryen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume29en_US
dc.description.number8en_US
dc.identifier.doi10.1111/j.1467-8659.2010.01717.xen_US
dc.identifier.pages2328-2339en_US


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