Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
| dc.contributor.author | Oeztireli, A. C. | en_US |
| dc.contributor.author | Guennebaud, G. | en_US |
| dc.contributor.author | Gross, M. | en_US |
| dc.date.accessioned | 2015-02-23T10:16:16Z | |
| dc.date.available | 2015-02-23T10:16:16Z | |
| dc.date.issued | 2009 | en_US |
| dc.description.abstract | Moving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smooth-out small or sharp features. In this paper, we address these major issues, and present a novel point based surface definition combining the simplicity of implicit MLS surfaces [SOS04,Kol05] with the strength of robust statistics. To reach this new definition, we review MLS surfaces in terms of local kernel regression, opening the doors to a vast and well established literature from which we utilize robust kernel regression. Our novel representation can handle sparse sampling, generates a continuous surface better preserving fine details, and can naturally handle any kind of sharp features with controllable sharpness. Finally, it combines ease of implementation with performance competing with other non-robust approaches. | en_US |
| dc.description.number | 2 | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.description.volume | 28 | en_US |
| dc.identifier.doi | 10.1111/j.1467-8659.2009.01388.x | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.pages | 493-501 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/j.1467-8659.2009.01388.x | en_US |
| dc.publisher | The Eurographics Association and Blackwell Publishing Ltd | en_US |
| dc.title | Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression | en_US |