IEICE TRANSACTIONS on Information
Scale-Space Processing of Point-Sampled Geometry for Efficient 3D Object Segmentation
Summary :
In this paper, we present a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities. Central to our method is the extension of the scale-space theory to the three-dimensional space to allow feature analysis and classification at different scales. Then, a new surface classifier is computed and used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and incomplete line features. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. Applications include 3D shape matching and retrieval, surface reconstruction and feature preserving simplification.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.5 pp.963-970
- Publication Date
- 2005/05/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.5.963
- Type of Manuscript
- Special Section PAPER (Special Section on Cyberworlds)
- Category
Authors
Keyword
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Hamid LAGA, Hiroki TAKAHASHI, Masayuki NAKAJIMA, "Scale-Space Processing of Point-Sampled Geometry for Efficient 3D Object Segmentation" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 5, pp. 963-970, May 2005, doi: 10.1093/ietisy/e88-d.5.963.
Abstract: In this paper, we present a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities. Central to our method is the extension of the scale-space theory to the three-dimensional space to allow feature analysis and classification at different scales. Then, a new surface classifier is computed and used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and incomplete line features. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. Applications include 3D shape matching and retrieval, surface reconstruction and feature preserving simplification.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.5.963/_p
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@ARTICLE{e88-d_5_963,
author={Hamid LAGA, Hiroki TAKAHASHI, Masayuki NAKAJIMA, },
journal={IEICE TRANSACTIONS on Information},
title={Scale-Space Processing of Point-Sampled Geometry for Efficient 3D Object Segmentation},
year={2005},
volume={E88-D},
number={5},
pages={963-970},
abstract={In this paper, we present a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities. Central to our method is the extension of the scale-space theory to the three-dimensional space to allow feature analysis and classification at different scales. Then, a new surface classifier is computed and used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and incomplete line features. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. Applications include 3D shape matching and retrieval, surface reconstruction and feature preserving simplification.},
keywords={},
doi={10.1093/ietisy/e88-d.5.963},
ISSN={},
month={May},}
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TY - JOUR
TI - Scale-Space Processing of Point-Sampled Geometry for Efficient 3D Object Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 963
EP - 970
AU - Hamid LAGA
AU - Hiroki TAKAHASHI
AU - Masayuki NAKAJIMA
PY - 2005
DO - 10.1093/ietisy/e88-d.5.963
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2005
AB - In this paper, we present a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities. Central to our method is the extension of the scale-space theory to the three-dimensional space to allow feature analysis and classification at different scales. Then, a new surface classifier is computed and used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and incomplete line features. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. Applications include 3D shape matching and retrieval, surface reconstruction and feature preserving simplification.
ER -
English
