Sparse Non-rigid Registration of 3D Shapes

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dc.contributor.authorYang, Jingyuen_US
dc.contributor.authorLi, Keen_US
dc.contributor.authorLi, Kunen_US
dc.contributor.authorLai, Yu-Kunen_US
dc.contributor.editorMirela Ben-Chen and Ligang Liuen_US
dc.date.accessioned2015-07-06T05:00:35Z
dc.date.available2015-07-06T05:00:35Z
dc.date.issued2015en_US
dc.description.abstractNon-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an l2-norm regularization on the local transformation differences. However, the l2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodnessof- fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an l1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise.en_US
dc.description.number5en_US
dc.description.sectionheadersRegistrationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12699en_US
dc.identifier.pages089-099en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12699en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectHierarchy and geometric transformationsen_US
dc.titleSparse Non-rigid Registration of 3D Shapesen_US
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34-Issue 5
SGP15: Eurographics Symposium on Geometry Processing (CGF 34-5)