模量约束:一种新的约束自标定方法

M. Pollefeys, L. Gool, A. Oosterlinck
{"title":"模量约束:一种新的约束自标定方法","authors":"M. Pollefeys, L. Gool, A. Oosterlinck","doi":"10.1109/ICPR.1996.546047","DOIUrl":null,"url":null,"abstract":"To obtain a Euclidean reconstruction from images the cameras have to be calibrated. In recent years different approaches have been proposed to avoid explicit calibration. The problem with these methods is that several parameters have to be retrieved at once. Because of the non-linearity of the equations this is not an easy task and the methods often fail to converge. In the's paper a stratified approach is proposed which allows to first retrieve the affine calibration of the camera using the modulus constraint. Having the affine calibration it is easy to upgrade to Euclidean. The important advantage of this method is that only three parameters have to be evaluated at first. From a practical point of view, the major gain is that an affine reconstruction is obtained from arbitrary sequences of views, whereas so far affine reconstruction has been based on pairs of views with a pure translation in between. A short illustration of another application is also given. Once the affine calibration is known, the constraint can be used to retrieve the Euclidean calibration in the presence of a variable focal length.","PeriodicalId":290297,"journal":{"name":"Proceedings of 13th International Conference on Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"129","resultStr":"{\"title\":\"The modulus constraint: a new constraint self-calibration\",\"authors\":\"M. Pollefeys, L. Gool, A. Oosterlinck\",\"doi\":\"10.1109/ICPR.1996.546047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To obtain a Euclidean reconstruction from images the cameras have to be calibrated. In recent years different approaches have been proposed to avoid explicit calibration. The problem with these methods is that several parameters have to be retrieved at once. Because of the non-linearity of the equations this is not an easy task and the methods often fail to converge. In the's paper a stratified approach is proposed which allows to first retrieve the affine calibration of the camera using the modulus constraint. Having the affine calibration it is easy to upgrade to Euclidean. The important advantage of this method is that only three parameters have to be evaluated at first. From a practical point of view, the major gain is that an affine reconstruction is obtained from arbitrary sequences of views, whereas so far affine reconstruction has been based on pairs of views with a pure translation in between. A short illustration of another application is also given. Once the affine calibration is known, the constraint can be used to retrieve the Euclidean calibration in the presence of a variable focal length.\",\"PeriodicalId\":290297,\"journal\":{\"name\":\"Proceedings of 13th International Conference on Pattern Recognition\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"129\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 13th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1996.546047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 13th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1996.546047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 129

摘要

为了从图像中获得欧几里得重建,相机必须进行校准。近年来,人们提出了不同的方法来避免显式校准。这些方法的问题是必须一次检索多个参数。由于方程的非线性,这不是一件容易的事,而且方法往往不能收敛。本文提出了一种分层方法,该方法允许首先使用模量约束检索相机的仿射校准。具有仿射校准,便于升级到欧几里得。这种方法的重要优点是首先只需要计算三个参数。从实际的角度来看,主要的收获是从任意序列的视图中获得仿射重建,而到目前为止,仿射重建是基于视图对之间的纯平移。还给出了另一种应用的简短说明。一旦知道了仿射校准,约束就可以用来在可变焦距的情况下检索欧几里得校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The modulus constraint: a new constraint self-calibration
To obtain a Euclidean reconstruction from images the cameras have to be calibrated. In recent years different approaches have been proposed to avoid explicit calibration. The problem with these methods is that several parameters have to be retrieved at once. Because of the non-linearity of the equations this is not an easy task and the methods often fail to converge. In the's paper a stratified approach is proposed which allows to first retrieve the affine calibration of the camera using the modulus constraint. Having the affine calibration it is easy to upgrade to Euclidean. The important advantage of this method is that only three parameters have to be evaluated at first. From a practical point of view, the major gain is that an affine reconstruction is obtained from arbitrary sequences of views, whereas so far affine reconstruction has been based on pairs of views with a pure translation in between. A short illustration of another application is also given. Once the affine calibration is known, the constraint can be used to retrieve the Euclidean calibration in the presence of a variable focal length.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信