基于局部坐标正则化的鲁棒模板非刚体运动跟踪。

Wei Li, Shang Zhao, Xiao Xiao, James K Hahn
{"title":"基于局部坐标正则化的鲁棒模板非刚体运动跟踪。","authors":"Wei Li, Shang Zhao, Xiao Xiao, James K Hahn","doi":"10.1109/wacv45572.2020.9093533","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions. We propose our tracking strategies for different surface regions to minimize misalignments and reduce error accumulation. This method can thus preserve local geometric features and prevent undesirable distortions. Moreover, we introduce a geodesic-based correspondence estimation algorithm to align surfaces with large displacements. Finally, we demonstrate the effectiveness of our proposed method with detailed experiments.</p>","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"2020 ","pages":"390-399"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/wacv45572.2020.9093533","citationCount":"3","resultStr":"{\"title\":\"Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization.\",\"authors\":\"Wei Li, Shang Zhao, Xiao Xiao, James K Hahn\",\"doi\":\"10.1109/wacv45572.2020.9093533\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions. We propose our tracking strategies for different surface regions to minimize misalignments and reduce error accumulation. This method can thus preserve local geometric features and prevent undesirable distortions. Moreover, we introduce a geodesic-based correspondence estimation algorithm to align surfaces with large displacements. Finally, we demonstrate the effectiveness of our proposed method with detailed experiments.</p>\",\"PeriodicalId\":73325,\"journal\":{\"name\":\"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision\",\"volume\":\"2020 \",\"pages\":\"390-399\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/wacv45572.2020.9093533\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/wacv45572.2020.9093533\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/5/14 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/wacv45572.2020.9093533","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/5/14 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

在本文中,我们提出了基于模板的非刚性配准算法来解决单台或多台商品深度相机帧对帧运动跟踪中的不对准问题。在非刚性配准中,我们分析了相邻节点的局部坐标中的变形,并利用这种微分表示来表示变形场的正则化项。基于表面区域的跟踪状态,每对相邻节点的局部坐标正则化是不同的。我们提出了针对不同表面区域的跟踪策略,以最大限度地减少不对准和减少误差累积。因此,该方法可以保留局部几何特征并防止不必要的扭曲。此外,我们还引入了一种基于测地线的对应估计算法来对大位移曲面。最后,通过详细的实验验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization.

In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions. We propose our tracking strategies for different surface regions to minimize misalignments and reduce error accumulation. This method can thus preserve local geometric features and prevent undesirable distortions. Moreover, we introduce a geodesic-based correspondence estimation algorithm to align surfaces with large displacements. Finally, we demonstrate the effectiveness of our proposed method with detailed experiments.

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
期刊最新文献
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction. Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images. PathLDM: Text conditioned Latent Diffusion Model for Histopathology. Domain Generalization with Correlated Style Uncertainty. Semantic-aware Video Representation for Few-shot Action Recognition.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1