使用kinect V2传感器恢复超高清摄像机深度的线性方法

Yuan Gao, M. Ziegler, Frederik Zilly, Sandro Esquivel, R. Koch
{"title":"使用kinect V2传感器恢复超高清摄像机深度的线性方法","authors":"Yuan Gao, M. Ziegler, Frederik Zilly, Sandro Esquivel, R. Koch","doi":"10.23919/MVA.2017.7986908","DOIUrl":null,"url":null,"abstract":"Depth-Image-Based Rendering (DIBR) is a mature and important method for making free-viewpoint videos. As for the study of the DIBR approach, on the one hand, most of current research focuses on how to use it in systems with low resolution cameras, while a lot of Ultra HD rendering devices have been launched into markets. On the other hand, the quality and accuracy of the depth image directly affects the final rendering result. Therefore, in this paper we try to make some improvements on solving the problem of recovering the depth information for Ultra HD cameras with the help of a Kinect V2 sensor. To this end, a linear least squares method is proposed, which recovers the rigid transformation between a Kinect V2 and an Ultra HD camera, using the depth information from the Kinect V2 sensor. In addition, a non-linear coarse-to-fine method, which is based on Sparse Bundle Adjustment (SBA), is compared with this linear method. Experiments show that our proposed method performs better than the non-linear method for the Ultra HD depth image recovery both in computing time and precision.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"934 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A linear method for recovering the depth of Ultra HD cameras using a kinect V2 sensor\",\"authors\":\"Yuan Gao, M. Ziegler, Frederik Zilly, Sandro Esquivel, R. Koch\",\"doi\":\"10.23919/MVA.2017.7986908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Depth-Image-Based Rendering (DIBR) is a mature and important method for making free-viewpoint videos. As for the study of the DIBR approach, on the one hand, most of current research focuses on how to use it in systems with low resolution cameras, while a lot of Ultra HD rendering devices have been launched into markets. On the other hand, the quality and accuracy of the depth image directly affects the final rendering result. Therefore, in this paper we try to make some improvements on solving the problem of recovering the depth information for Ultra HD cameras with the help of a Kinect V2 sensor. To this end, a linear least squares method is proposed, which recovers the rigid transformation between a Kinect V2 and an Ultra HD camera, using the depth information from the Kinect V2 sensor. In addition, a non-linear coarse-to-fine method, which is based on Sparse Bundle Adjustment (SBA), is compared with this linear method. Experiments show that our proposed method performs better than the non-linear method for the Ultra HD depth image recovery both in computing time and precision.\",\"PeriodicalId\":193716,\"journal\":{\"name\":\"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)\",\"volume\":\"934 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/MVA.2017.7986908\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/MVA.2017.7986908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

基于深度图像的渲染(deep - image - based Rendering, DIBR)是制作自由视点视频的一种成熟而重要的方法。对于DIBR方法的研究,一方面,目前的研究大多集中在如何在低分辨率摄像机的系统中使用它,而许多超高清渲染设备已经投入市场。另一方面,深度图像的质量和精度直接影响最终的渲染效果。因此,在本文中,我们尝试在解决Kinect V2传感器对超高清相机深度信息恢复的问题上做一些改进。为此,提出了一种线性最小二乘法,利用Kinect V2传感器的深度信息恢复Kinect V2与超高清摄像机之间的刚性变换。此外,还将基于稀疏束调整(SBA)的非线性粗到精方法与线性方法进行了比较。实验表明,该方法在计算时间和精度上都优于非线性方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A linear method for recovering the depth of Ultra HD cameras using a kinect V2 sensor
Depth-Image-Based Rendering (DIBR) is a mature and important method for making free-viewpoint videos. As for the study of the DIBR approach, on the one hand, most of current research focuses on how to use it in systems with low resolution cameras, while a lot of Ultra HD rendering devices have been launched into markets. On the other hand, the quality and accuracy of the depth image directly affects the final rendering result. Therefore, in this paper we try to make some improvements on solving the problem of recovering the depth information for Ultra HD cameras with the help of a Kinect V2 sensor. To this end, a linear least squares method is proposed, which recovers the rigid transformation between a Kinect V2 and an Ultra HD camera, using the depth information from the Kinect V2 sensor. In addition, a non-linear coarse-to-fine method, which is based on Sparse Bundle Adjustment (SBA), is compared with this linear method. Experiments show that our proposed method performs better than the non-linear method for the Ultra HD depth image recovery both in computing time and precision.
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:481959085
Book学术官方微信