3D modeling of virtualized reality objects using neural computing

A methodology for 3D modeling of virtualized reality objects using neural computing is presented. In this paper the objects are represented in virtualized reality and their 3D data are acquired by one of three acquisition systems: endoneurosonographic equipment (ENS), stereo vision system and non-contact 3D digitizer. These objects are modeled by one of three neural architectures: Multilayer Feed-Forward Neural Network (MLFFNN), Self-Organizing Maps (SOM) and Neural Gas Network (NGN). The 3D virtualized representations correspond to several objects as phantom brain tumors, faces, archaeological items, fruits, among others. We carry out comparisons in terms of computational cost, architectural complexity, training method, training epochs and performance. Finally, we present the modeling results and conclude that SOM and NGN models achieve the best performances and the lowest displaying times, while MLFFNN models have the lowest memory requirements and acceptable training times.