Out-of-core compression for gigantic polygon meshes

M. Isenburg, S. Gumhold
{"title":"Out-of-core compression for gigantic polygon meshes","authors":"M. Isenburg, S. Gumhold","doi":"10.1145/1201775.882366","DOIUrl":null,"url":null,"abstract":"Polygonal models acquired with emerging 3D scanning technology or from large scale CAD applications easily reach sizes of several gigabytes and do not fit in the address space of common 32-bit desktop PCs. In this paper we propose an out-of-core mesh compression technique that converts such gigantic meshes into a streamable, highly compressed representation. During decompression only a small portion of the mesh needs to be kept in memory at any time. As full connectivity information is available along the decompression boundaries, this provides seamless mesh access for incremental in-core processing on gigantic meshes. Decompression speeds are CPU-limited and exceed one million vertices and two million triangles per second on a 1.8 GHz Athlon processor.A novel external memory data structure provides our compression engine with transparent access to arbitrary large meshes. This out-of-core mesh was designed to accommodate the access pattern of our region-growing based compressor, which - in return - performs mesh queries as seldom and as local as possible by remembering previous queries as long as needed and by adapting its traversal slightly. The achieved compression rates are state-of-the-art.","PeriodicalId":314969,"journal":{"name":"ACM SIGGRAPH 2003 Papers","volume":"297 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"165","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2003 Papers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1201775.882366","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 165

Abstract

Polygonal models acquired with emerging 3D scanning technology or from large scale CAD applications easily reach sizes of several gigabytes and do not fit in the address space of common 32-bit desktop PCs. In this paper we propose an out-of-core mesh compression technique that converts such gigantic meshes into a streamable, highly compressed representation. During decompression only a small portion of the mesh needs to be kept in memory at any time. As full connectivity information is available along the decompression boundaries, this provides seamless mesh access for incremental in-core processing on gigantic meshes. Decompression speeds are CPU-limited and exceed one million vertices and two million triangles per second on a 1.8 GHz Athlon processor.A novel external memory data structure provides our compression engine with transparent access to arbitrary large meshes. This out-of-core mesh was designed to accommodate the access pattern of our region-growing based compressor, which - in return - performs mesh queries as seldom and as local as possible by remembering previous queries as long as needed and by adapting its traversal slightly. The achieved compression rates are state-of-the-art.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
巨大多边形网格的核外压缩
通过新兴的3D扫描技术或大规模CAD应用程序获得的多边形模型很容易达到几gb的大小,并且不适合普通32位桌面pc的地址空间。在本文中,我们提出了一种核外网格压缩技术,将这种巨大的网格转换为可流的、高度压缩的表示。在解压过程中,任何时候只有一小部分网格需要保存在内存中。由于沿着解压缩边界可以获得完整的连接信息,这为在巨大网格上进行增量核内处理提供了无缝的网格访问。解压速度受到cpu的限制,在1.8 GHz的Athlon处理器上每秒超过100万个顶点和200万个三角形。一种新的外部存储数据结构为我们的压缩引擎提供了对任意大网格的透明访问。这种核外网格的设计是为了适应我们基于区域增长的压缩器的访问模式,作为回报,它通过尽可能长时间地记住以前的查询并稍微调整其遍历,尽可能少地执行网格查询,并尽可能局部地执行查询。实现的压缩率是最先进的。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Session details: Modeling and simplification Session details: Points Session details: Shadows Session details: Character animation Session details: Design and depiction
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1