Accurate and Efficient Computation of Laplacian Spectral Distances and Kernels

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dc.contributor.authorPatané, Giuseppeen_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2017-03-13T18:13:02Z
dc.date.available2017-03-13T18:13:02Z
dc.date.issued2017
dc.description.abstractThis paper introduces the Laplacian spectral distances, as a function that resembles the usual distance map, but exhibits properties (e.g. smoothness, locality, invariance to shape transformations) that make them useful to processing and analysing geometric data. Spectral distances are easily defined through a filtering of the Laplacian eigenpairs and reduce to the heat diffusion, wave, biharmonic and commute‐time distances for specific filters. In particular, the smoothness of the spectral distances and the encoding of local and global shape properties depend on the convergence of the filtered eigenvalues to zero. Instead of applying a truncated spectral approximation or prolongation operators, we propose a computation of Laplacian distances and kernels through the solution of sparse linear systems. Our approach is free of user‐defined parameters, overcomes the evaluation of the Laplacian spectrum and guarantees a higher approximation accuracy than previous work.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.12794
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12794
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12794
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectmodelling
dc.subjectdigital geometry processing
dc.subjectgeometric modelling
dc.subjectComputer Graphics [Computing methodologies]: Shape modelling—
dc.titleAccurate and Efficient Computation of Laplacian Spectral Distances and Kernelsen_US
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