Bounds and error estimates for radiosity

Dani Lischinski, Brian E. Smits, D. Greenberg
{"title":"Bounds and error estimates for radiosity","authors":"Dani Lischinski, Brian E. Smits, D. Greenberg","doi":"10.1145/192161.192176","DOIUrl":null,"url":null,"abstract":"We present a method for determining a posteriori bounds and estimates for local and total errors in radiosity solutions. The ability to obtain bounds and estimates for the total error is crucial fro reliably judging the acceptability of a solution. Realistic estimates of the local error improve the efficiency of adaptive radiosity algorithms, such as hierarchical radiosity, by indicating where adaptive refinement is necessary. First, we describe a hierarchical radiosity algorithm that computes conservative lower and upper bounds on the exact radiosity function, as well as on the approximate solution. These bounds account for the propagation of errors due to interreflections, and provide a conservative upper bound on the error. We also describe a non-conservative version of the same algorithm that is capable of computing tighter bounds, from which more realistic error estimates can be obtained. Finally, we derive an expression for the effect of a particular interaction on the total error. This yields a new error-driven refinement strategy for hierarchical radiosity, which is shown to be superior to brightness-weighted refinement.","PeriodicalId":151245,"journal":{"name":"Proceedings of the 21st annual conference on Computer graphics and interactive techniques","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"131","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st annual conference on Computer graphics and interactive techniques","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/192161.192176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 131

Abstract

We present a method for determining a posteriori bounds and estimates for local and total errors in radiosity solutions. The ability to obtain bounds and estimates for the total error is crucial fro reliably judging the acceptability of a solution. Realistic estimates of the local error improve the efficiency of adaptive radiosity algorithms, such as hierarchical radiosity, by indicating where adaptive refinement is necessary. First, we describe a hierarchical radiosity algorithm that computes conservative lower and upper bounds on the exact radiosity function, as well as on the approximate solution. These bounds account for the propagation of errors due to interreflections, and provide a conservative upper bound on the error. We also describe a non-conservative version of the same algorithm that is capable of computing tighter bounds, from which more realistic error estimates can be obtained. Finally, we derive an expression for the effect of a particular interaction on the total error. This yields a new error-driven refinement strategy for hierarchical radiosity, which is shown to be superior to brightness-weighted refinement.
辐射的边界和误差估计
我们提出了一种方法来确定后验界和估计的局部和总误差的辐射解决方案。为了可靠地判断解的可接受性,获得总误差的范围和估计的能力是至关重要的。局部误差的实际估计提高了自适应辐射算法的效率,如分层辐射,通过指示哪里需要自适应改进。首先,我们描述了一种分层辐射算法,该算法计算精确辐射函数的保守下界和上界以及近似解。这些边界考虑了由于相互反射引起的误差传播,并提供了误差的保守上界。我们还描述了同一算法的非保守版本,它能够计算更严格的边界,从中可以获得更真实的误差估计。最后,我们推导出了一个特定相互作用对总误差影响的表达式。这产生了一种新的误差驱动的分层辐射细化策略,该策略优于亮度加权细化。
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