Confidence-Based Large-Scale Dense Multi-View Stereo
文献类型：期刊论文
| 作者 | Li, Zhaoxin1,2; Zuo, Wangmeng3; Wang, Zhaoqi2; Zhang, Lei1 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2020 |
| 卷号 | 29页码:7176-7191 |
| 关键词 | Multi-view stereo confidence large-scale interpolation static and dynamic guidance refinement |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2020.2999853 |
| 英文摘要 | Albeit remarkable progress has been made to improve the accuracy and completeness of multi-view stereo (MVS), existing methods still suffer from either sparse reconstructions of low-textured surfaces or heavy computational burden. In this paper, we propose a Confidence-based Large-scale Dense Multi-view Stereo (CLD-MVS) method for high resolution imagery. Firstly, we formulate MVS as a multi-view depth estimation problem, and employ a normal-aware efficient PatchMatch stereo to estimate the initial depth and normal map for each reference view. A self-supervised deep learning method is then developed to predict the spatial confidence for multi-view depth maps, which is combined with cross-view consistency to generate the ground control points. Subsequently, a confidence-driven and boundary-aware interpolation scheme using static and dynamic guidance is adopted to synthesize dense depth and normal maps. Finally, a refinement procedure which leverages synthesized depth and normal as prior is conducted to estimate cross-view consistent surface. Experiments show that the proposed CLD-MVS method achieves high geometric completeness while preserving fine-scale details. In particular, it has ranked No. 1 on the ETH3D high-resolution MVS benchmark in terms of F-1-score. |
| 资助项目 | NVIDIA Corporation |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000546910100021 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15088]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Zhaoxin |
| 作者单位 | 1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhaoxin,Zuo, Wangmeng,Wang, Zhaoqi,et al. Confidence-Based Large-Scale Dense Multi-View Stereo[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:7176-7191. |
| APA | Li, Zhaoxin,Zuo, Wangmeng,Wang, Zhaoqi,&Zhang, Lei.(2020).Confidence-Based Large-Scale Dense Multi-View Stereo.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,7176-7191. |
| MLA | Li, Zhaoxin,et al."Confidence-Based Large-Scale Dense Multi-View Stereo".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):7176-7191. |
入库方式： OAI收割
来源：计算技术研究所
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