DDGPnP:处理异常值的基于差分度图的 PnP 解决方案

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhichao Cui , Zeqi Chen , Chi Zhang , Gaofeng Meng , Yuehu Liu , Xiangmo Zhao
{"title":"DDGPnP:处理异常值的基于差分度图的 PnP 解决方案","authors":"Zhichao Cui ,&nbsp;Zeqi Chen ,&nbsp;Chi Zhang ,&nbsp;Gaofeng Meng ,&nbsp;Yuehu Liu ,&nbsp;Xiangmo Zhao","doi":"10.1016/j.cviu.2024.104130","DOIUrl":null,"url":null,"abstract":"<div><p>Existing external relationships for outlier removal in the perspective-n-point problem are generally spatial coherence among the neighbor correspondences. In the situation of high noise or spatially incoherent distributions, pose estimation is relatively inaccurate due to a small number of detected inliers. To address these problems, this paper explores the globally coherent external relationships for outlier removal and pose estimation. To this end, the differential degree graph (DDG) is proposed to employ the intersection angles between rays of correspondences to handle outliers. Firstly, a pair of two degree graphs are constructed to establish the external relationships between 3D-2D correspondences in the world and camera coordinates. Secondly, the DDG is estimated through subtracting the two degree graphs and operating binary operation with a degree threshold. Besides, this paper mathematically proves that the maximum clique of the DDG represents the inliers. Thirdly, a novel vertice degree based method is put forward to extract the maximum clique from DDG for outlier removal. Besides, this paper proposes a novel pipeline of DDG based PnP solution, i.e. DDGPnP, to achieve accurate pose estimation. Experiments demonstrate the superiority and effectiveness of the proposed method in the aspects of outlier removal and pose estimation by comparison with the state of the arts. Especially for the high noise situation, the DDGPnP method can achieve not only accurate pose but also a large number of correct correspondences.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"248 ","pages":"Article 104130"},"PeriodicalIF":4.3000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DDGPnP: Differential degree graph based PnP solution to handle outliers\",\"authors\":\"Zhichao Cui ,&nbsp;Zeqi Chen ,&nbsp;Chi Zhang ,&nbsp;Gaofeng Meng ,&nbsp;Yuehu Liu ,&nbsp;Xiangmo Zhao\",\"doi\":\"10.1016/j.cviu.2024.104130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Existing external relationships for outlier removal in the perspective-n-point problem are generally spatial coherence among the neighbor correspondences. In the situation of high noise or spatially incoherent distributions, pose estimation is relatively inaccurate due to a small number of detected inliers. To address these problems, this paper explores the globally coherent external relationships for outlier removal and pose estimation. To this end, the differential degree graph (DDG) is proposed to employ the intersection angles between rays of correspondences to handle outliers. Firstly, a pair of two degree graphs are constructed to establish the external relationships between 3D-2D correspondences in the world and camera coordinates. Secondly, the DDG is estimated through subtracting the two degree graphs and operating binary operation with a degree threshold. Besides, this paper mathematically proves that the maximum clique of the DDG represents the inliers. Thirdly, a novel vertice degree based method is put forward to extract the maximum clique from DDG for outlier removal. Besides, this paper proposes a novel pipeline of DDG based PnP solution, i.e. DDGPnP, to achieve accurate pose estimation. Experiments demonstrate the superiority and effectiveness of the proposed method in the aspects of outlier removal and pose estimation by comparison with the state of the arts. Especially for the high noise situation, the DDGPnP method can achieve not only accurate pose but also a large number of correct correspondences.</p></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":\"248 \",\"pages\":\"Article 104130\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision and Image Understanding\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S107731422400211X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S107731422400211X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

在透视点问题中,用于去除异常点的现有外部关系通常是相邻对应关系之间的空间一致性。在高噪声或空间分布不一致的情况下,由于检测到的异常值数量较少,姿态估计相对不准确。为解决这些问题,本文探讨了用于离群点去除和姿态估计的全局一致性外部关系。为此,本文提出了差分度图(DDG),利用对应射线之间的交角来处理异常值。首先,构建一对两度图,以建立世界和摄像机坐标中 3D-2D 对应点之间的外部关系。其次,通过对两个度数图进行减法运算,并利用度数阈值进行二进制运算,从而估算出 DDG。此外,本文还从数学角度证明了 DDG 的最大簇代表离群值。第三,本文提出了一种新颖的基于顶点度的方法,从 DDG 中提取最大克团以去除离群值。此外,本文还提出了一种基于 DDG 的 PnP 解决方案,即 DDGPnP,以实现精确的姿态估计。实验证明,与现有技术相比,本文提出的方法在离群点去除和姿态估计方面具有优越性和有效性。特别是在高噪声情况下,DDGPnP 方法不仅能获得准确的姿态,还能获得大量正确的对应关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DDGPnP: Differential degree graph based PnP solution to handle outliers

Existing external relationships for outlier removal in the perspective-n-point problem are generally spatial coherence among the neighbor correspondences. In the situation of high noise or spatially incoherent distributions, pose estimation is relatively inaccurate due to a small number of detected inliers. To address these problems, this paper explores the globally coherent external relationships for outlier removal and pose estimation. To this end, the differential degree graph (DDG) is proposed to employ the intersection angles between rays of correspondences to handle outliers. Firstly, a pair of two degree graphs are constructed to establish the external relationships between 3D-2D correspondences in the world and camera coordinates. Secondly, the DDG is estimated through subtracting the two degree graphs and operating binary operation with a degree threshold. Besides, this paper mathematically proves that the maximum clique of the DDG represents the inliers. Thirdly, a novel vertice degree based method is put forward to extract the maximum clique from DDG for outlier removal. Besides, this paper proposes a novel pipeline of DDG based PnP solution, i.e. DDGPnP, to achieve accurate pose estimation. Experiments demonstrate the superiority and effectiveness of the proposed method in the aspects of outlier removal and pose estimation by comparison with the state of the arts. Especially for the high noise situation, the DDGPnP method can achieve not only accurate pose but also a large number of correct correspondences.

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
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
×
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