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SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions

文献类型：期刊论文

作者Liu, Kunhua1; Zhang, Yunqing2; Xie, Yuting2; Li, Leixin2; Wang, Yutong3; Chen, Long3
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期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
DOI10.1109/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.
WOS关键词INTELLIGENT VEHICLES ; ARTIFICIAL-INTELLIGENCE ; OBJECT REMOVAL ; SEGMENTATION
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研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:001173317800016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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
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
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.

入库方式： OAI收割

来源：自动化研究所

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