Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction | |
Liu, Pengpeng1,2; Yu, Tao3,4; Zeng, Zhi2; Liu, Yebin3,4; Zhang, Guixuan2; Song, Zhen5 | |
发表期刊 | WIRELESS COMMUNICATIONS & MOBILE COMPUTING |
ISSN | 1530-8669 |
2022-02-11 | |
卷号 | 2022页码:9 |
摘要 | To achieve a promising performance on relative pose estimation for RGB-D scans, a considerable overlap between two RGB-D inputs is often required for most existing methods. However, in many practical applications for human scans, we often have to estimate the relative poses under arbitrary overlaps, which is challenging for existing methods. To deal with this problem, this paper presents a novel end-to-end and coarse-to-fine optimization method. Our method is self-supervision which firstly combines implicit function reconstruction with differentiable render for RGB-D human input scans at arbitrary overlaps in relative pose estimation. The insight is to take advantage of the underlying human geometry prior as much as possible. First of all, for stable coarse poses, we utilize the implicit function reconstruction to dig out abundant hidden cues from unseen regions in the initialization module. To further refine the poses, the differentiable render is leveraged to establish a self-supervision mechanism in the optimization module, which is independent of standard pipelines for feature extracting and accurate correspondence matching. More importantly, our proposed method is flexible to be extended to multiview input scans. The results and evaluations demonstrate that our optimization module is robust for real-world noisy inputs, and our approach outperforms considerably than standard pipelines in non-overlapping setups. |
DOI | 10.1155/2022/4351951 |
关键词[WOS] | REGISTRATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Outstanding Young Scientist Program[BJJWZYJH01201910048035] ; Fundamental Research Funds for the Central Universities[YNZDA1805] |
项目资助者 | Beijing Outstanding Young Scientist Program ; Fundamental Research Funds for the Central Universities |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS记录号 | WOS:000766931800002 |
出版者 | WILEY-HINDAWI |
七大方向——子方向分类 | 三维视觉 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48006 |
专题 | 数字内容技术与服务研究中心_版权智能与文化计算 |
通讯作者 | Song, Zhen |
作者单位 | 1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, Beijing, Peoples R China 3.Tsinghua Univ, Dept Automat, Beijing, Peoples R China 4.Tsinghua Univ, BNRist, Beijing, Peoples R China 5.Cent Acad Drama, Adv Res Ctr Digitalizat Tradit Drama, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Pengpeng,Yu, Tao,Zeng, Zhi,et al. Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction[J]. WIRELESS COMMUNICATIONS & MOBILE COMPUTING,2022,2022:9. |
APA | Liu, Pengpeng,Yu, Tao,Zeng, Zhi,Liu, Yebin,Zhang, Guixuan,&Song, Zhen.(2022).Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction.WIRELESS COMMUNICATIONS & MOBILE COMPUTING,2022,9. |
MLA | Liu, Pengpeng,et al."Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction".WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022(2022):9. |
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