An Efficient Method for Very Large Scale Out-of-Core Terrain Visualization

Huijie Zhang, Jigui Sun, Haihong Yu, Changsong Qi
{"title":"An Efficient Method for Very Large Scale Out-of-Core Terrain Visualization","authors":"Huijie Zhang, Jigui Sun, Haihong Yu, Changsong Qi","doi":"10.1109/ICAT.2006.36","DOIUrl":null,"url":null,"abstract":"Large scale terrain visualization with high- resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio.","PeriodicalId":133842,"journal":{"name":"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAT.2006.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Large scale terrain visualization with high- resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio.
一种大规模核外地形可视化的有效方法
高分辨率的大尺度地形可视化在许多研究领域有着日益增长的需求。为实现地形的高效绘制,提出了一种基于多分辨率存储技术的核外地形可视化方法。在外部存储器中,地形数据集从上到下进行细分,构建基于四叉树的多分辨率分层结构。分层结构可以减少必须加载到内部存储器中的高程数据。这样可以大大提高I/O访问的效率。此外,为了实现实时地形立交桥数据的快速检索,提出了一种高效的索引算法,该算法根据静态误差相似度和封闭空间约束将分层结构中的节点划分为若干簇。此外,本文还提出了一种无裂纹的方法。在GTOP30数据集上进行的综合实验表明,该方法在效率和简化率方面都优于Block和Hierarchy算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信