Implicit Surface Modelling with a Globally Regularised Basis of Compact Support
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The contributions of the paper include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable interpolation properties previously only associated with fully supported bases. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem lying at the core of kernel-based machine learning methods. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data and four-dimensional interpolation between three-dimensional shapes.
Author(s): | Walder, C. and Schölkopf, B. and Chapelle, O. |
Journal: | Computer Graphics Forum |
Volume: | 25 |
Number (issue): | 3 |
Pages: | 635-644 |
Year: | 2006 |
Month: | September |
Day: | 0 |
Bibtex Type: | Article (article) |
DOI: | 10.1111/j.1467-8659.2006.00983.x |
Digital: | 0 |
Electronic Archiving: | grant_archive |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
BibTex
@article{3958, title = {Implicit Surface Modelling with a Globally Regularised Basis of Compact Support}, journal = {Computer Graphics Forum}, abstract = {We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The contributions of the paper include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable interpolation properties previously only associated with fully supported bases. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem lying at the core of kernel-based machine learning methods. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data and four-dimensional interpolation between three-dimensional shapes.}, volume = {25}, number = {3}, pages = {635-644}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = sep, year = {2006}, slug = {3958}, author = {Walder, C. and Sch{\"o}lkopf, B. and Chapelle, O.}, month_numeric = {9} }