Survey of lightweighting methods of huge 3D models for online Web3D visualization

Q1 Computer Science Virtual Reality Intelligent Hardware Pub Date : 2023-10-01 DOI:10.1016/j.vrih.2020.02.002
Xiaojun Liu , Jinyuan Jia , Chang Liu
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引用次数: 0

Abstract

Background

With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper.

Methods

By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of lightweighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization.

Results

By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic.

Conclusions

Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache management, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.

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面向Web3D在线可视化的海量三维模型轻量化方法研究
背景随着Web3D技术的快速发展,在线Web3D可视化,特别是对复杂模型或场景的可视化需求越来越大。由于在处理这些庞大模型时,Web3D系统负载和资源消耗之间存在重大冲突,本文综述了用于在线Web3D可视化的庞大3D模型轻量级方法。方法通过观察建模过程中人为操作引入的几何冗余,阐述了Web3D可视化中几类旨在减少数据量和资源消耗的轻量级相关工作。结果通过比较视角,总结了每种方法的特点,在综述的方法中,通过检测和去除重复分量来实现轻量级目标的几何冗余去除方法是当前在线Web3D可视化的合适方法。同时,学习算法目前仍处于改进期,是我们期待的未来研究课题。结论在一种高效的轻量级在线Web3D可视化方法中,应考虑原始数据的特性、现有方法的组合或扩展、调度策略、缓存管理和渲染机制等多个方面。同时,创新方法,特别是学习算法,值得探索。
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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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