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Non-Iterative, Feature-Preserving Mesh Smoothing

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Show simple item record Jones, Thouis R. Durand, Frédo Desbrun, Mathieu 2003-12-13T19:39:26Z 2003-12-13T19:39:26Z 2004-01
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

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