High quality depth map estimation of object surface from light-field images
文献类型：期刊论文
| 作者 | Liu, Fei1,2 ; Hou, Guangqi2; Sun, Zhenan2; Tan, Tieniu2; Guangqi Hou
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2017-08-23 |
| 卷号 | 252期号:252页码:3-16 |
| 关键词 | Light Field Depth Estimation Stereo Matching Disparity Refinement |
| DOI | 10.1016/j.neucom.2016.09.136 |
| 文献子类 | Article |
| 英文摘要 | ; Light-field imaging provides a novel solution to the passive 3D imaging technology. However the dense multi-view sub-aperture images decoded from the light-field raw image have extremely narrow baselines, which lead to inconsistent matching with terrible blurriness and ambiguities. This paper presents an accurate depth estimation algorithm for object surface using a lenslet light-field camera. The input data for depth estimation can be both light-field videos and images under indoor and outdoor environment. To tackle the continuously changing outdoor illumination and take full advantage of rays, rendering enhancement is performed through denoising and local vignetting correction for obtaining high-fidelity 4D light fields. The novel sub-aperture image pair selection and stereo matching algorithm are proposed for disparity computation. Then we apply the disparity refinement for recovering high quality surface details and handling disparity discontinuities. Finally both commercial and self-developed light-field cameras are used to capture real-world scenes with various lighting conditions and poses. The accuracy and robustness of the proposed algorithm are evaluated both on synthetic light-field datasets and real-world scenes by comparing with state-of-the-art algorithms. The experimental results show that high quality depth maps are recovered with smooth surfaces and accurate geometry structures. (C) 2017 Elsevier B.V. All rights reserved. |
| WOS关键词 | BELIEF PROPAGATION ; STEREO |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000405884500002 |
| 资助机构 | National Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61420106015 ; 61302184 ; 61273272) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19632]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Guangqi Hou |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit,Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Fei,Hou, Guangqi,Sun, Zhenan,et al. High quality depth map estimation of object surface from light-field images[J]. NEUROCOMPUTING,2017,252(252):3-16. |
| APA | Liu, Fei,Hou, Guangqi,Sun, Zhenan,Tan, Tieniu,&Guangqi Hou.(2017).High quality depth map estimation of object surface from light-field images.NEUROCOMPUTING,252(252),3-16. |
| MLA | Liu, Fei,et al."High quality depth map estimation of object surface from light-field images".NEUROCOMPUTING 252.252(2017):3-16. |
入库方式： OAI收割
来源：自动化研究所
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