dc.contributor.author | Jones, Thouis R. | |
dc.contributor.author | Durand, Frédo | |
dc.contributor.author | Desbrun, Mathieu | |
dc.date.accessioned | 2003-12-13T19:39:26Z | |
dc.date.available | 2003-12-13T19:39:26Z | |
dc.date.issued | 2004-01 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/3866 | |
dc.description.abstract | With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes. | en |
dc.description.sponsorship | Singapore-MIT Alliance (SMA) | en |
dc.format.extent | 8331712 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.relation.ispartofseries | Computer Science (CS); | |
dc.subject | mesh smoothing | en |
dc.subject | robust statistics | en |
dc.subject | mollification | en |
dc.subject | feature preservation | en |
dc.title | Non-Iterative, Feature-Preserving Mesh Smoothing | en |
dc.type | Article | en |