SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions | |
Liu, Kunhua3; Zhang, Yunqing2; Xie, Yuting2; Li, Leixin2; Wang, Yutong1; Chen, Long1 | |
刊名 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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2024 | |
卷号 | 9期号:1页码:69-78 |
关键词 | Image edge detection Feature extraction Point cloud compression Generators Maintenance engineering Vehicle dynamics Autonomous vehicles GAN generator discriminator SCRGB-D dataset synergistic RGB-D images inpainting |
ISSN号 | 2379-8858 |
DOI | 10.1109/TIV.2023.3326236 |
通讯作者 | Chen, Long(long.chen@ia.ac.cn) |
英文摘要 | Map inpainting is an important technology in the production of maps for autonomous driving vehicles. In recent years, scholars have often used methods such as point cloud inpainting, RGB image inpainting, and depth inpainting to repair maps. However, these methods require high computational power and result in longer algorithmic processing times. To address this issue, we propose SynerFill, a synergistic RGB-D images inpainting method that can simultaneously inpaint RGB and depth images. We design its network architecture and loss functions, which include a generator, an RGB image discriminator, a depth image discriminator, and an edge image discriminator. Second, we collect real-world data and build a large-scale, multi-scene, multi-weather dataset called the Synthetic City RGB-D (SCRGB-D) Dataset based on 3ds Max, CARLA, and Unreal Engine 4. To verify SynerFill, we conduct experiments on the SCRGB-D dataset, DynaFill dataset, and SceneNet dataset. The experimental results show that SynerFill achieves state-of-the-art (SOTA) performance. |
资助项目 | National Natural Science Foundation of China[62006256] ; National Natural Science Foundation of China[62373356] ; Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Qingdao University of Technology), Ministry of Education |
WOS关键词 | INTELLIGENT VEHICLES ; ARTIFICIAL-INTELLIGENCE ; OBJECT REMOVAL ; SEGMENTATION |
WOS研究方向 | Computer Science ; Engineering ; Transportation |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001173317800016 |
资助机构 | National Natural Science Foundation of China ; Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Qingdao University of Technology), Ministry of Education |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/58704] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Chen, Long |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China 3.Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Kunhua,Zhang, Yunqing,Xie, Yuting,et al. SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(1):69-78. |
APA | Liu, Kunhua,Zhang, Yunqing,Xie, Yuting,Li, Leixin,Wang, Yutong,&Chen, Long.(2024).SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(1),69-78. |
MLA | Liu, Kunhua,et al."SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.1(2024):69-78. |
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