Proceedings: GI 2013

Least-squares hermite radial basis functions implicits with adaptive sampling

Harlen Batagelo , João Gois

Proceedings of Graphics Interface 2013: Regina, Saskatchewan, Canada, 29 - 31 May 2013, 109-116

DOI 10.20380/GI2013.15

  • Bibtex

    @inproceedings{Batagelo:2013:10.20380/GI2013.15,
    author = {Batagelo, Harlen and Gois, Jo{\~a}o},
    title = {Least-squares hermite radial basis functions implicits with adaptive sampling},
    booktitle = {Proceedings of Graphics Interface 2013},
    series = {GI 2013},
    year = {2013},
    issn = {0713-5424},
    isbn = {978-1-4822-1680-6},
    location = {Regina, Saskatchewan, Canada},
    pages = {109--116},
    numpages = {8},
    doi = {10.20380/GI2013.15},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

Abstract

We investigate the use of Hermite Radial Basis Functions (HRBF) Implicits with least squares for the implicit surface reconstruction of scattered first-order Hermitian data. Instead of interpolating all pairs of point-normals, we select a small subset of point-normals as centers of the HRBF Implicits while considering all pairs as least-squares constraints. Centers are adaptively sampled via a novel greedy algorithm that takes into account Hermitian data and distances between points. This approach produces sets of centers that are globally well distributed and preserves local features. We show that this yields accurate surface reconstructions with small sets of centers.