by Thierry Guillemot, Andres Almansa, Tamy Boubekeur
Abstract:
We introduce a non local point set surface model for mesh less geometry processing. Compared to previous approaches, our model better preserves features by exploiting self-similarities present in natural and man-made 3D shapes. The basic idea is to decompose 3D samples into scalar displacements over a coarse smooth domain. Then, considering the displacement field stemming from the local neighboring set of a given point, we collect similar functions over the entire model and define a specific displacement value for the point by the mean of similarity-based weighted combination of them. The underlying scale-space decomposition allows for a wide range of similarity metrics, while scalar displacements simplify rotation-invariant registration of the local sample sets. Our contribution is a non local extension of all previous point set surface models, which (i) improves feature preservation by exploiting self-similarities, iif present, and (ii) boils down to the underlying (local) point set surface model, when self-similarities are not strong enough. We evaluate our approach against state-of-the-art point set surface models and demonstrate its ability to better preserve details in the presence of noise and highly varying sampling rates. We apply it to several data sets, in the context of typical point-based applications.
Reference:
Non Local Point Set Surfaces (Thierry Guillemot, Andres Almansa, Tamy Boubekeur), In (3DIMPVT 2012) Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, IEEE, 2012.
Bibtex Entry:
@inproceedings{Guillemot2012:3DIMPVT:NLPSS,
Abstract = {We introduce a non local point set surface model for mesh less geometry processing. Compared to previous approaches, our model better preserves features by exploiting self-similarities present in natural and man-made 3D shapes. The basic idea is to decompose 3D samples into scalar displacements over a coarse smooth domain. Then, considering the displacement field stemming from the local neighboring set of a given point, we collect similar functions over the entire model and define a specific displacement value for the point by the mean of similarity-based weighted combination of them. The underlying scale-space decomposition allows for a wide range of similarity metrics, while scalar displacements simplify rotation-invariant registration of the local sample sets. Our contribution is a non local extension of all previous point set surface models, which (i) improves feature preservation by exploiting self-similarities, iif present, and (ii) boils down to the underlying (local) point set surface model, when self-similarities are not strong enough. We evaluate our approach against state-of-the-art point set surface models and demonstrate its ability to better preserve details in the presence of noise and highly varying sampling rates. We apply it to several data sets, in the context of typical point-based applications.},
Address = {Z\"{u}rich},
Author = {Guillemot, Thierry and Almansa, Andres and Boubekeur, Tamy},
Booktitle = {(3DIMPVT 2012) Second International Conference on 3D Imaging, Modeling, Processing, Visualization \& Transmission},
Date-Added = {2015-02-18 16:53:40 +0000},
Date-Modified = {2015-02-18 16:53:40 +0000},
Doi = {10.1109/3DIMPVT.2012.71},
Isbn = {978-0-7695-4873-9},
Keywords = {3D sample decomposition,Approximation methods,Filtering,Image reconstruction,Kernel,Noise,Noise measurement,Noise reduction,Non-Local methods,Point Set Surfaces,Reconstruction,Surface reconstruction,coarse smooth domain,displacement field,image registration,man-made 3D shape,mesh less geometry processing,natural 3D shape,nonlocal point set surface,rotation-invariant registration,scalar displacement,scale-space decomposition,similarity metrics,similarity-based weighted combination,smoothing methods},
Month = oct,
Pages = {324--331},
Publisher = {IEEE},
Shorttitle = {3D Imaging, Modeling, Processing, Visualization an},
Title = {{Non Local Point Set Surfaces}},
Url = {http://perso.telecom-paristech.fr/~boubek/papers/NLPSS/},
Year = {2012},
Bdsk-Url-1 = {http://perso.telecom-paristech.fr/~boubek/papers/NLPSS/},
Bdsk-Url-2 = {http://dx.doi.org/10.1109/3DIMPVT.2012.71}}