An adaptive learning approach for 3-D surface reconstruction from point clouds.
IEEE Trans Neural Netw
; 19(6): 1130-40, 2008 Jun.
Article
em En
| MEDLINE
| ID: mdl-18541509
In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Adaptação Fisiológica
/
Imageamento Tridimensional
/
Aprendizagem
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Neural Netw
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos