Return to search

Non-Iterative, Feature-Preserving Mesh Smoothing

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. / Singapore-MIT Alliance (SMA)

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/3866
Date01 1900
CreatorsJones, Thouis R., Durand, Frédo, Desbrun, Mathieu
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
TypeArticle
Format8331712 bytes, application/pdf
RelationComputer Science (CS);

Page generated in 0.0015 seconds