CEB-Collaboratively Multi-Granularity Interest Scheduling Algorithm for Loading Large WebBIM Scene

Q3 Computer Science

计算机辅助设计与图形学学报 Pub Date : 2021-09-01 DOI:10.3724/sp.j.1089.2021.18718

Ke Li, Qian Zhang, Jinyuan Jia

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引用次数: 1

Abstract

With the rise of 5G and VR, the demand on visualizing large-scale BIM scene on mobile web browsers is increasing rapidly. However, due to the weakness of browser rendering and networking delay, it is still rather challenging to access huge Web3D scenes smoothly. Firstly, edge computing is introduced into WebBIM online visualization, and a cloud-edge-browser (CEB) collaboratively multi-granularity WebBIM framework is designed. Secondly, the interest degree is defined for each of BIM components and the multi-granularity WebBIM transmission scheduling is proposed based on interest degree. Then, the optimal viewpoint selection for initial loading is presented in terms of globality of viewing scope, light weightness and interest degree. Finally, the experimental platform is deployed using Alibaba Cloud service, and some large-scale BIM scenes are used for practical transmission testing. Experimental results show that the proposed algorithm can effectively reduce the data amount of loading WebBIM scenes. The initial loading speed is also improved significantly, and there is no obvious delay to occur when users move huge WebBIM scenes through freely.

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面向大型WebBIM场景加载的协同多粒度兴趣调度算法

随着5G和VR的兴起，在移动web浏览器上可视化大规模BIM场景的需求正在快速增长。然而，由于浏览器渲染和网络延迟的弱点，想要流畅地访问庞大的Web3D场景仍然是一个很大的挑战。首先，将边缘计算引入WebBIM的在线可视化中，设计了一个云边缘浏览器协同的多粒度WebBIM框架。其次，定义了BIM各组件的兴趣度，并基于兴趣度提出了多粒度的WebBIM传输调度方案;然后，从全局视角、轻权重和兴趣度三个方面提出了初始加载的最优视点选择;最后，利用阿里云服务部署实验平台，并使用一些大规模的BIM场景进行实际传输测试。实验结果表明，该算法可以有效地减少WebBIM场景的加载数据量。初始加载速度也有明显提高，当用户自由移动巨大的WebBIM场景时，不会出现明显的延迟。

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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