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Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey

文献类型：期刊论文

作者Xie, Zexiao1; Yu, Xiaoxuan1; Gao, Xiang1,4; Li, Kunqian1; Shen, Shuhan2,3,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-09-02
页码21
关键词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
ISSN号2162-237X
DOI10.1109/TNNLS.2022.3201534
通讯作者Gao, Xiang(xgao@ouc.edu.cn) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词SINGLE IMAGE ; LIDAR DATA ; NETWORK ; FUSION ; FEATURES ; MODEL
资助项目National Science Foundation of China[62003319] ; National Science Foundation of China[42076192] ; National Science Foundation of China[62076026] ; Shandong Provincial Natural Science Foundation[ZR2020QF075]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000852238700001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Science Foundation of China ; Shandong Provincial Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/50113]  
专题精密感知与控制研究中心_精密感知与控制
自动化研究所_模式识别国家重点实验室_机器人视觉团队
中科院工业视觉智能装备工程实验室
通讯作者Gao, Xiang; Shen, Shuhan
作者单位1.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat CASIA, SenseTime Res Grp, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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