Título: Evaluating approximations generated by the GNG3D method for mesh simplification
Autores: Navarro, Pedro
Tortosa Grau, Leandro
Vicent Francés, José Francisco
Zamora Gómez, Antonio
Fecha: 2012-11-23
2012-11-23
2008
Publicador: RUA Docencia
Fuente:
Tipo: info:eu-repo/semantics/conferenceObject
Tema: Surface simplification
Mesh reconstruction
Error approximations
Neural networks
Growing neural gas
Growing cell structures
Ciencia de la Computación e Inteligencia Artificial
Descripción: In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.
The research was supported by the University of Alicante, (GV06/018).
Idioma: Inglés

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