A landmark-free approach for automatic, dense and robust correspondence of 3D faces
文献类型：期刊论文
| 作者 | Fan, Zhenfeng2,3 ; Hu, Xiyuan4; Chen, Chen1,2; Wang, Xiaolian1,2; Peng, Silong1,2
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| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2023 |
| 卷号 | 133页码:14 |
| ISSN号 | 0031-3203 |
| 关键词 | 3D face Dense correspondence Non -rigid registration |
| DOI | 10.1016/j.patcog.2022.108971 |
| 通讯作者 | Hu, Xiyuan(huxy@njust.edu.cn) |
| 英文摘要 | Global dense registration of 3D faces commonly prioritizes correspondences of facial landmarks which are fiducial points for the anatomical structures. However, it is not always easy to pre-annotate the land-marks accurately in raw scans of 3D faces. Contrary to the current state-of-the-art in dense 3D face cor-respondence, we propose a general framework without pre-annotated landmarks, which promotes its ro-bustness and allows the meshes to deform in a uniform manner. The proposed framework includes two stages: first the correspondences are established using a template face; and then we select some well -reconstructed samples to build a prior model and leverage it into the correspondence process of other samples. In both stages, the dense registration is revisited in two perspectives: semantic and topological correspondence. In the latter stage, we further incorporate shape and normal statistics of 3D faces to reg-ularize the correspondence process for more robust results. This provides a feasible way to handle data with noises and occlusions, as well as large deformation caused by facial expressions. Our basic idea is to gradually refine the correspondence of individual points in a way global-to-local. At the same time, we solve the local-to-global deformation based on the refined correspondences. The two processes are alternated, and aided by some confidence checks for each individual points. In the experiments, the pro-posed method is evaluated both qualitatively and quantitatively on three datasets including two publicly available ones: FRGC v2.0 and BU-3DFE datasets, demonstrating its effectiveness.(c) 2022 Elsevier Ltd. All rights reserved. |
| WOS关键词 | RECOGNITION ; REGISTRATION ; MODELS ; POINT |
| 资助项目 | National Science Foundation of China[NSFC 62106250] ; China Postdoctoral Science Foundation[2021M703272] ; Liaoning Collaboration Innovation Center |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000863094500008 |
| 资助机构 | National Science Foundation of China ; China Postdoctoral Science Foundation ; Liaoning Collaboration Innovation Center |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50355]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_多维数据分析团队 |
| 通讯作者 | Hu, Xiyuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Nanjing Univ Sci & Technol, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fan, Zhenfeng,Hu, Xiyuan,Chen, Chen,et al. A landmark-free approach for automatic, dense and robust correspondence of 3D faces[J]. PATTERN RECOGNITION,2023,133:14. |
| APA | Fan, Zhenfeng,Hu, Xiyuan,Chen, Chen,Wang, Xiaolian,&Peng, Silong.(2023).A landmark-free approach for automatic, dense and robust correspondence of 3D faces.PATTERN RECOGNITION,133,14. |
| MLA | Fan, Zhenfeng,et al."A landmark-free approach for automatic, dense and robust correspondence of 3D faces".PATTERN RECOGNITION 133(2023):14. |
入库方式： OAI收割
来源：自动化研究所
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