Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction
文献类型：期刊论文
| 作者 | Liu, Pengpeng4,5 ; Yu, Tao2,3; Zeng, Zhi4; Liu, Yebin2,3; Zhang, Guixuan4; Song, Zhen1
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| 刊名 | WIRELESS COMMUNICATIONS & MOBILE COMPUTING
 |
| 出版日期 | 2022-02-11 |
| 卷号 | 2022页码:9 |
| ISSN号 | 1530-8669 |
| DOI | 10.1155/2022/4351951 |
| 通讯作者 | Song, Zhen(songzhen@zhongxi.cn) |
| 英文摘要 | 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. |
| WOS关键词 | REGISTRATION |
| 资助项目 | Beijing Outstanding Young Scientist Program[BJJWZYJH01201910048035] ; Fundamental Research Funds for the Central Universities[YNZDA1805] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | WILEY-HINDAWI |
| WOS记录号 | WOS:000766931800002 |
| 资助机构 | Beijing Outstanding Young Scientist Program ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48005]  |
| 专题 | 数字内容技术与服务研究中心_新媒体服务与管理技术 |
| 通讯作者 | Song, Zhen |
| 作者单位 | 1.Cent Acad Drama, Adv Res Ctr Digitalizat Tradit Drama, Beijing, Peoples R China 2.Tsinghua Univ, BNRist, Beijing, Peoples R China 3.Tsinghua Univ, Dept Automat, Beijing, Peoples R China 4.Chinese Acad Sci CASIA, Inst Automat, Beijing, Peoples R China 5.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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