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

doi:10.1109/TIV.2023.3326236

CASIA OpenIR > 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

	SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions
	Liu, Kunhua 1; Zhang, Yunqing 2; Xie, Yuting 2; Li, Leixin 2; Wang, Yutong3 ; Chen, Long3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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.