Deep Learning Classification in web3D model geometries Using X3D models for Machine Learning Classification in Real-Time web applications

Abstract

In this paper we study about the requirements of web3D models and particular X3D formatted models in order to work efficiently with Deep Learning algorithms. The reason we are focusing in this particular type of 3D models is that we consider web3D as part of the future in computer graphics. The introduction of metaverse™ technology, indeed confirms that lightweight interoperable 3D models will be an essential part of many novel services we will see in the near future. Furthermore, X3D language is expressing 3D information in a way semantically friendly and so very useful for future applications. In our research we conclude that the lightweight X3D models require some vertices enhancement in order to cooperate with Deep Learning algorithms, however we suggest algorithms that may be applied and make the whole process in Real-Time which is very important in case of web applications.