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

Chrysoula Tzermia, Nick-Periklis Chourdas, A. Malamos
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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.
web3D模型几何中的深度学习分类:在实时web应用程序中使用X3D模型进行机器学习分类
本文研究了web3D模型和特定的X3D格式模型的要求,以便与深度学习算法有效地协同工作。我们专注于这种特殊类型的3D模型的原因是我们认为web3D是计算机图形学未来的一部分。metaverse™技术的引入确实证实了轻量级互操作3D模型将成为我们在不久的将来看到的许多新颖服务的重要组成部分。此外,X3D语言以语义友好的方式表达3D信息,因此对未来的应用程序非常有用。在我们的研究中,我们得出结论,轻量级的X3D模型需要一些顶点增强才能与深度学习算法合作,但是我们建议可以应用的算法,并使整个过程实时化,这在web应用的情况下非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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