phase - renet:一个相位回归网络,用于从相机校准的散焦模式中检测特征。

Applied optics Pub Date : 2025-09-20 DOI:10.1364/AO.564036
Wenhui Hou, Chuanqi Zhou, Haojie Zhu, Dashan Zhang, Yuwei Wang, Gao Jing, Lu Liu
{"title":"phase - renet:一个相位回归网络,用于从相机校准的散焦模式中检测特征。","authors":"Wenhui Hou, Chuanqi Zhou, Haojie Zhu, Dashan Zhang, Yuwei Wang, Gao Jing, Lu Liu","doi":"10.1364/AO.564036","DOIUrl":null,"url":null,"abstract":"<p><p>Conventional camera calibration typically requires acquiring clear and focused target images for accurate feature detection. Defocused target images may reduce the feature detection accuracy and even lead to failure in estimating camera parameters. To address this issue, this paper employs the crossed fringe as a calibration pattern and develops an effective phase regression network (Phase-ReNet) for wrapped phase calibration, from which feature points can be extracted with high precision. Unlike traditional phase-shifting methods, which require multiple patterns, our method recovers horizontal and vertical phase maps using just a single pattern, significantly improving calibration efficiency. Experimental results demonstrate that this method can achieve feature detection accuracy comparable to traditional phase-shifting methods, and the mean reprojection errors of the defocused camera are only 0.0552 pixels. These results highlight that our method is suitable for defocused camera calibration tasks.</p>","PeriodicalId":101299,"journal":{"name":"Applied optics","volume":"64 27","pages":"7957-7967"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phase-ReNet: a phase regression network for feature detection from a defocused pattern for camera calibration.\",\"authors\":\"Wenhui Hou, Chuanqi Zhou, Haojie Zhu, Dashan Zhang, Yuwei Wang, Gao Jing, Lu Liu\",\"doi\":\"10.1364/AO.564036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Conventional camera calibration typically requires acquiring clear and focused target images for accurate feature detection. Defocused target images may reduce the feature detection accuracy and even lead to failure in estimating camera parameters. To address this issue, this paper employs the crossed fringe as a calibration pattern and develops an effective phase regression network (Phase-ReNet) for wrapped phase calibration, from which feature points can be extracted with high precision. Unlike traditional phase-shifting methods, which require multiple patterns, our method recovers horizontal and vertical phase maps using just a single pattern, significantly improving calibration efficiency. Experimental results demonstrate that this method can achieve feature detection accuracy comparable to traditional phase-shifting methods, and the mean reprojection errors of the defocused camera are only 0.0552 pixels. These results highlight that our method is suitable for defocused camera calibration tasks.</p>\",\"PeriodicalId\":101299,\"journal\":{\"name\":\"Applied optics\",\"volume\":\"64 27\",\"pages\":\"7957-7967\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/AO.564036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/AO.564036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的相机校准通常需要获取清晰和聚焦的目标图像,以准确地检测特征。散焦的目标图像会降低特征检测的精度,甚至导致相机参数估计失败。针对这一问题,本文采用交叉条纹作为定标模式,开发了一种有效的相位回归网络(phase - renet)进行包裹相位定标,可以高精度地提取特征点。与传统移相方法需要多种模式不同,该方法仅使用单一模式即可恢复水平和垂直相位图,显著提高了校准效率。实验结果表明,该方法可以达到与传统相移方法相当的特征检测精度,离焦相机的平均重投影误差仅为0.0552像素。结果表明,该方法适用于散焦相机标定任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phase-ReNet: a phase regression network for feature detection from a defocused pattern for camera calibration.

Conventional camera calibration typically requires acquiring clear and focused target images for accurate feature detection. Defocused target images may reduce the feature detection accuracy and even lead to failure in estimating camera parameters. To address this issue, this paper employs the crossed fringe as a calibration pattern and develops an effective phase regression network (Phase-ReNet) for wrapped phase calibration, from which feature points can be extracted with high precision. Unlike traditional phase-shifting methods, which require multiple patterns, our method recovers horizontal and vertical phase maps using just a single pattern, significantly improving calibration efficiency. Experimental results demonstrate that this method can achieve feature detection accuracy comparable to traditional phase-shifting methods, and the mean reprojection errors of the defocused camera are only 0.0552 pixels. These results highlight that our method is suitable for defocused camera calibration tasks.

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