Knowledge Commons of Institute of Automation,CAS
SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions | |
Liu, Kunhua1; Zhang, Yunqing2; Xie, Yuting2; Li, Leixin2; Wang, Yutong3![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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ISSN | 2379-8858 |
2024 | |
卷号 | 9期号:1页码:69-78 |
通讯作者 | Chen, Long(long.chen@ia.ac.cn) |
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. |
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 |
DOI | 10.1109/TIV.2023.3326236 |
关键词[WOS] | INTELLIGENT VEHICLES ; ARTIFICIAL-INTELLIGENCE ; OBJECT REMOVAL ; SEGMENTATION |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | National Natural Science Foundation of China ; Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Qingdao University of Technology), Ministry of Education |
WOS研究方向 | Computer Science ; Engineering ; Transportation |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:001173317800016 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/58705 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 多模态人工智能系统全国重点实验室_医疗机器人 |
通讯作者 | Chen, Long |
作者单位 | 1.Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China 2.Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
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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