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 radical...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/3866 |
| _version_ | 1811078826330947584 |
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| author | Jones, Thouis R. Durand, Frédo Desbrun, Mathieu |
| author_facet | Jones, Thouis R. Durand, Frédo Desbrun, Mathieu |
| author_sort | Jones, Thouis R. |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T11:06:05Z |
| format | Article |
| id | mit-1721.1/3866 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:06:05Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/38662019-04-12T08:36:54Z Non-Iterative, Feature-Preserving Mesh Smoothing Jones, Thouis R. Durand, Frédo Desbrun, Mathieu mesh smoothing robust statistics mollification feature preservation 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) 2003-12-13T19:39:26Z 2003-12-13T19:39:26Z 2004-01 Article http://hdl.handle.net/1721.1/3866 en_US Computer Science (CS); 8331712 bytes application/pdf application/pdf |
| spellingShingle | mesh smoothing robust statistics mollification feature preservation Jones, Thouis R. Durand, Frédo Desbrun, Mathieu Non-Iterative, Feature-Preserving Mesh Smoothing |
| title | Non-Iterative, Feature-Preserving Mesh Smoothing |
| title_full | Non-Iterative, Feature-Preserving Mesh Smoothing |
| title_fullStr | Non-Iterative, Feature-Preserving Mesh Smoothing |
| title_full_unstemmed | Non-Iterative, Feature-Preserving Mesh Smoothing |
| title_short | Non-Iterative, Feature-Preserving Mesh Smoothing |
| title_sort | non iterative feature preserving mesh smoothing |
| topic | mesh smoothing robust statistics mollification feature preservation |
| url | http://hdl.handle.net/1721.1/3866 |
| work_keys_str_mv | AT jonesthouisr noniterativefeaturepreservingmeshsmoothing AT durandfredo noniterativefeaturepreservingmeshsmoothing AT desbrunmathieu noniterativefeaturepreservingmeshsmoothing |