Résumé: There are several open problems that are viewed as a bottleneck in the reverse engineering process: ( 1) The topology is unknown; therefore, point connectivity relations are unde .ned. ( 2) The .tted surface must satisfy global and local shape preservation criteria, which are unde .ned explicitly. The reconstruction is based on parameterization and .tting stages. However, the above problems are in .uenced mainly by the parameterization. To overcome the above problems, the neural network self- organizing map ( SOM) method is proposed for creating a 3D parametric grid. The main advantage of the SOM method is that it detects the orientation of the grid and the position of the sub- boundaries. Then through an adaptive process the neural network grid is converged to the sampled shape. The SOM method is applied directly on a 3D grid and avoids projection anomalies, which are common to other methods. For the surface .tting stage the random surface error correction .tting method, which is based on the SOM method, was developed and implemented.

Langue: Anglais