Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
KAUST Grant NumberKUK-I1-007-43
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AbstractWe consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data. © 2013 Springer-Verlag.
CitationLellmann J, Morel J-M, Schönlieb C-B (2013) Anisotropic Third-Order Regularization for Sparse Digital Elevation Models. Scale Space and Variational Methods in Computer Vision: 161–173. Available: http://dx.doi.org/10.1007/978-3-642-38267-3_14.
SponsorsThe authors would like to thank Andrea Bertozzi andAlex Chen for helpful discussions. This publication is based on work supportedby Award No. KUK-I1-007-43, made by King Abdullah University of Scienceand Technology (KAUST), EPSRC first grant No. EP/J009539/1, EPSRC/IsaacNewton Trust Small Grant, and Royal Society International Exchange AwardNo. IE110314. J.-M. Morel was supported by MISS project of Centre Nationald’Etudes Spatiales, the Office of Naval Research under Grant N00014-97-1-0839and by the European Research Council, advanced grant “Twelve labours”.
PublisherSpringer Science + Business Media