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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
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2022-09-02
Pages21
Corresponding AuthorGao, Xiang(xgao@ouc.edu.cn) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn)
AbstractDepth 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.
KeywordTask 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
DOI10.1109/TNNLS.2022.3201534
WOS KeywordSINGLE IMAGE ; LIDAR DATA ; NETWORK ; FUSION ; FEATURES ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[62003319] ; National Science Foundation of China[42076192] ; National Science Foundation of China[62076026] ; Shandong Provincial Natural Science Foundation[ZR2020QF075]
Funding OrganizationNational Science Foundation of China ; Shandong Provincial Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000852238700001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://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 AuthorGao, Xiang; Shen, Shuhan
Affiliation1.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 AffilicationInstitute 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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