High accuracy and geometry-consistent confidence prediction network for multi-view stereo
文献类型：期刊论文
| 作者 | Li, Zhaoxin2; Zhang, Xiaoge2; Wang, Kangkan1; Jiang, Hao2; Wang, Zhaoqi2 |
| 刊名 | COMPUTERS & GRAPHICS-UK
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 97页码:148-159 |
| 关键词 | 3D reconstruction Confidence prediction Multi-view stereo PatchMatch stereo |
| ISSN号 | 0097-8493 |
| DOI | 10.1016/j.cag.2021.04.020 |
| 英文摘要 | Confidence prediction task attempts to infer the correctness of estimated depth hypotheseshich has gained popularity recently in stereo matching and boosts the accuracy of disparity estimation. However, less attention is paid on confidence prediction of multi-view stereo (MVS), where multi-view depth estimation is a key step for high-quality reconstruction. In this work, we propose a Geometry-consistent Confidence prediction Network (GeoConfNet), where the correctness of a depth hypothesis is accurately predicted via a deep neural network that explores both spatial coherence and cross-view consistency. The proposed deep network consists of a feature extraction module, a U-Net-based fusion module and a confidence refinement module. Furthermore, we demonstrate that truncated signed distance field (TSDF) is a powerful cross-view feature which can be an effective complement to spatial features, thereby remarkably boosting confidence prediction accuracy of MVS. Exhaustive experiments on a variety of MVS datasets as well as stereo matching datasets clearly demonstrate that our method achieves significantly better performance than state-of-the-art methods in terms of area under the curve (AUC). (c) 2021 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Key Research and Development Program of China[2018AAA0103002] ; National Key Research and Development Program of China[2017YFB1002600] ; National Natural Science Foundation of China[61702482] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000661427000003 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17651]  |
| 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; Jiang, Hao |
| 作者单位 | 1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhaoxin,Zhang, Xiaoge,Wang, Kangkan,et al. High accuracy and geometry-consistent confidence prediction network for multi-view stereo[J]. COMPUTERS & GRAPHICS-UK,2021,97:148-159. |
| APA | Li, Zhaoxin,Zhang, Xiaoge,Wang, Kangkan,Jiang, Hao,&Wang, Zhaoqi.(2021).High accuracy and geometry-consistent confidence prediction network for multi-view stereo.COMPUTERS & GRAPHICS-UK,97,148-159. |
| MLA | Li, Zhaoxin,et al."High accuracy and geometry-consistent confidence prediction network for multi-view stereo".COMPUTERS & GRAPHICS-UK 97(2021):148-159. |
入库方式： OAI收割
来源：计算技术研究所
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