Non-Iterative, Feature-Preserving Mesh Smoothing

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3866

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DC Field	Value	Language
dc.creator	Jones, Thouis R.	-
dc.creator	Durand, Frédo	-
dc.creator	Desbrun, Mathieu	-
dc.date	2003-12-13T19:39:26Z	-
dc.date	2003-12-13T19:39:26Z	-
dc.date	2004-01	-
dc.date.accessioned	2013-10-09T02:32:53Z	-
dc.date.available	2013-10-09T02:32:53Z	-
dc.date.issued	2013-10-09	-
dc.identifier	http://hdl.handle.net/1721.1/3866	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	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.	-
dc.description	Singapore-MIT Alliance (SMA)	-
dc.format	8331712 bytes	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	Computer Science (CS);	-
dc.subject	mesh smoothing	-
dc.subject	robust statistics	-
dc.subject	mollification	-
dc.subject	feature preservation	-
dc.title	Non-Iterative, Feature-Preserving Mesh Smoothing	-
dc.type	Article	-
Appears in Collections:	MIT Items

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