Sharp feature preserving MLS surface reconstruction based on local feature line approximations

Christopher Weber 1, * Stefanie Hahmann 2, * Hans Hagen 1 Georges-Pierre Bonneau 3
* Auteur correspondant
2 IMAGINE - Intuitive Modeling and Animation for Interactive Graphics & Narrative Environments
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 MAVERICK - Models and Algorithms for Visualization and Rendering
LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble , Inria Grenoble - Rhône-Alpes
Abstract : Sharp features in manufactured and designed objects require particular attention when reconstructing surfaces from unorganized scan point sets using moving least squares (MLS) fitting. It's an inherent property of MLS fitting that sharp features are smoothed out. Instead of searching for appropriate new fitting functions our approach computes a modified local point neighborhood so that a standard MLS fitting can be applied enhanced by sharp features reconstruction. We present a two-stage algorithm. In a pre-processing step sharp feature points are marked first. This algorithm is robust to noise since it is based on Gauss map clustering. In the main phase, the selected feature points are used to locally approximate the feature curve and to segment and enhance the local point neighborhood. The MLS projection thus leads to a piecewise smooth surface preserving all sharp features. The method is simple to implement and able to preserve line-type features as well as corner-type features during reconstruction.
Type de document :
Article dans une revue
Graphical Models, Elsevier, 2012, 74 (6), pp.335-345. <10.1016/j.gmod.2012.04.012>



https://hal.inria.fr/hal-00695492
Contributeur : Stefanie Hahmann <>
Soumis le : mercredi 9 mai 2012 - 10:59:15
Dernière modification le : mercredi 14 décembre 2016 - 01:07:12
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Christopher Weber, Stefanie Hahmann, Hans Hagen, Georges-Pierre Bonneau. Sharp feature preserving MLS surface reconstruction based on local feature line approximations. Graphical Models, Elsevier, 2012, 74 (6), pp.335-345. <10.1016/j.gmod.2012.04.012>. <hal-00695492>

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