Image-Based Rendering for Large-Scale Outdoor Scenes With Fusion of Monocular and Multi-View Stereo Depth
文献类型：期刊论文
| 作者 | Liu, Shaohua1,2; Li, Minghao1; Zhang, Xiaona3; Liu, Shuang3,4; Li, Zhaoxin4; Liu, Jing3,4; Mao, Tianlu4 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:117551-117565 |
| 关键词 | Image-based rendering multi-view stereo monocular depth estimation view synthesis outdoor scenes |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.3004431 |
| 英文摘要 | Image-based rendering (IBR) attempts to synthesize novel views using a set of observed images. Some IBR approaches (such as light fields) have yielded impressive high-quality results on small-scale scenes with dense photo capture. However, available wide-baseline IBR methods are still restricted by the low geometric accuracy and completeness of multi-view stereo (MVS) reconstruction on low-textured and non-Lambertian surfaces. The issues become more significant in large-scale outdoor scenes due to challenging scene content, e.g., buildings, trees, and sky. To address these problems, we present a novel IBR algorithm that consists of two key components. First, we propose a novel depth refinement method that combines MVS depth maps with monocular depth maps predicted via deep learning. A lookup table remap is proposed for converting the scale of the monocular depths to be consistent with the scale of the MVS depths. Then, the rescaled monocular depth is used as the constraint in the minimum spanning tree (MST)-based nonlocal filter to refine the per-view MVS depth. Second, we present an efficient shape-preserving warping algorithm that uses superpixels to generate the warped images and blend expected novel views of scenes. The proposed method has been evaluated on public MVS and view synthesis datasets, as well as newly captured large-scale outdoor datasets. In comparison with state-of-the-art methods, the experimental results demonstrated that the proposed method can obtain more complete and reliable depth maps for the challenging large-scale outdoor scenes, thereby resulting in more promising novel view synthesis. |
| 资助项目 | National Key Research and Development Program of China[2018YFB1700905] ; Major Program of the National Natural Science Foundation of China[91938301] ; National Natural Science Foundation of China[61702482] ; National Natural Science Foundation of China[61802109] ; National Natural Science Foundation of China[61532002] ; National Defense Equipment Advance Research Shared Technology Program of China[41402050301-170441402065] ; Sichuan Science and Technology Major Project on New Generation Artificial Intelligence[2018GZDZX0034] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000549116300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15929]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Jing; Mao, Tianlu |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China 2.Univ Elect Sci & Technol China, Inst Elect & Informat Engn Guangdong, Dongguan 523808, Peoples R China 3.Hebei Normal Univ, Coll Comp & Cyber Secur, Shijiazhuang 050024, Hebei, Peoples R China 4.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Shaohua,Li, Minghao,Zhang, Xiaona,et al. Image-Based Rendering for Large-Scale Outdoor Scenes With Fusion of Monocular and Multi-View Stereo Depth[J]. IEEE ACCESS,2020,8:117551-117565. |
| APA | Liu, Shaohua.,Li, Minghao.,Zhang, Xiaona.,Liu, Shuang.,Li, Zhaoxin.,...&Mao, Tianlu.(2020).Image-Based Rendering for Large-Scale Outdoor Scenes With Fusion of Monocular and Multi-View Stereo Depth.IEEE ACCESS,8,117551-117565. |
| MLA | Liu, Shaohua,et al."Image-Based Rendering for Large-Scale Outdoor Scenes With Fusion of Monocular and Multi-View Stereo Depth".IEEE ACCESS 8(2020):117551-117565. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。