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)
Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/3866 |
Date | 01 1900 |
Creators | Jones, Thouis R., Durand, Frédo, Desbrun, Mathieu |
Source Sets | M.I.T. Theses and Dissertation |
Language | en_US |
Detected Language | English |
Type | Article |
Format | 8331712 bytes, application/pdf |
Relation | Computer Science (CS); |
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