Institutional Repository of Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
| Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey | |
Xie, Zexiao1; Yu, Xiaoxuan1; Gao, Xiang1,2 ; Li, Kunqian1; Shen, Shuhan2,3,4
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| Source Publication | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| ISSN | 2162-237X |
| 2022-09-02 | |
| Pages | 21 |
| Corresponding Author | Gao, Xiang(xgao@ouc.edu.cn) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn) |
| Abstract | Depth completion aims to recover pixelwise depth from incomplete and noisy depth measurements with or without the guidance of a reference RGB image. This task attracted considerable research interest due to its importance in various computer vision-based applications, such as scene understanding, autonomous driving, 3-D reconstruction, object detection, pose estimation, trajectory prediction, and so on. As the system input, an incomplete depth map is usually generated by projecting the 3-D points collected by ranging sensors, such as LiDAR in outdoor environments, or obtained directly from RGB-D cameras in indoor areas. However, even if a high-end LiDAR is employed, the obtained depth maps are still very sparse and noisy, especially in the regions near the object boundaries, which makes the depth completion task a challenging problem. To address this issue, a few years ago, conventional image processing-based techniques were employed to fill the holes and remove the noise from the relatively dense depth maps obtained by RGB-D cameras, while deep learning-based methods have recently become increasingly popular and inspiring results have been achieved, especially for the challenging situation of LiDAR-image-based depth completion. This article systematically reviews and summarizes the works related to the topic of depth completion in terms of input modalities, data fusion strategies, loss functions, and experimental settings, especially for the key techniques proposed in deep learning-based multiple input methods. On this basis, we conclude by presenting the current status of depth completion and discussing several prospects for its future research directions. |
| Keyword | Task analysis Learning systems Noise measurement Laser radar Image color analysis Deep learning Data integration Data fusion deep learning depth completion loss function RGB-D and LiDAR data |
| DOI | 10.1109/TNNLS.2022.3201534 |
| WOS Keyword | SINGLE IMAGE ; LIDAR DATA ; NETWORK ; FUSION ; FEATURES ; MODEL |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Science Foundation of China[62003319] ; National Science Foundation of China[42076192] ; National Science Foundation of China[62076026] ; Shandong Provincial Natural Science Foundation[ZR2020QF075] |
| Funding Organization | National Science Foundation of China ; Shandong Provincial Natural Science Foundation |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000852238700001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50114 |
| Collection | 精密感知与控制研究中心_精密感知与控制 模式识别国家重点实验室_机器人视觉 |
| Corresponding Author | Gao, Xiang; Shen, Shuhan |
| Affiliation | 1.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China 2.Chinese Acad Sci, Inst Automat CASIA, SenseTime Res Grp, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences; Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Xie, Zexiao,Yu, Xiaoxuan,Gao, Xiang,et al. Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:21. |
| APA | Xie, Zexiao,Yu, Xiaoxuan,Gao, Xiang,Li, Kunqian,&Shen, Shuhan.(2022).Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,21. |
| MLA | Xie, Zexiao,et al."Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):21. |
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