| |||||||
ShanghaiTech University Knowledge Management System
NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results | |
Cao, Mingdeng1,2; Mou, Chong2,3; Yu, Fanghua4; Wang, Xintao2; Zheng, Yinqiang1; Zhang, Jian3; Dong, Chao4; Li, Gen5; Shan, Ying2; Timofte, Radu6; Sun, Xiaopeng7; Li, Weiqi8; Sheng, Xuhan8; Chen, Bin8; Ma, Haoyu7; Cheng, Ming7; Zhao, Shijie7; Huang, Huaibo9,10; Zhou, Xiaoqiang9,11; Ai, Yuang9,12; He, Ran9,10,13 ![]() ![]() | |
2023 | |
会议录名称 | IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS |
ISSN | 2160-7508 |
卷号 | 2023-June |
页码 | 1731-1745 |
发表状态 | 已发表 |
DOI | 10.1109/CVPRW59228.2023.00174 |
摘要 | This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets. © 2023 IEEE. |
关键词 | Computer vision Optical resolving power 2D images Degradation process Field of views High quality Image super resolutions Magnification factors Omnidirectional image Performance Restoration process Video super-resolution |
会议名称 | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 |
会议地点 | Vancouver, BC, Canada |
会议日期 | June 18, 2023 - June 22, 2023 |
收录类别 | EI |
语种 | 英语 |
出版者 | IEEE Computer Society |
EI入藏号 | 20233714730753 |
EI主题词 | Image compression |
EISSN | 2160-7516 |
EI分类号 | 723.5 Computer Applications ; 741.1 Light/Optics ; 741.2 Vision |
原始文献类型 | Conference article (CA) |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348760 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院 |
通讯作者 | Cao, Mingdeng |
作者单位 | 1.The University of Tokyo, Japan 2.Arc Lab, Tencent Pcg, China 3.Peking University, China 4.Shenzhen Institute of Advanced Technology, Cas, China 5.Platform Technologies, Tencent Online Video, China 6.Computer Vision Lab, Ifi & Caidas, University of Würzburg, Germany 7.ByteDance, China 8.Peking University, Shenzhen Graduate School, China 9.Mais&cripac, Institute of Automation, Chinese Academy of Sciences, China 10.School of Artificial Intelligence, University of Chinese Academy of Sciences, China 11.University of Science and Technology of China, China 12.Beijing Institute of Technology, China 13.School of Information Science and Technology, ShanghaiTech University, China 14.Harbin Institute of Technology, China 15.Graduate Institute of Electronics Engineering, National Taiwan University, Taiwan 16.Department of Electrical Engineering, National Taiwan University, Taiwan 17.Graduate Institute of Communication Engineering, National Taiwan University, Taiwan 18.ServiceNow, United States 19.ShanghaiTech University, China 20.Meituan, China 21.Xiaomi Inc |
推荐引用方式 GB/T 7714 | Cao, Mingdeng,Mou, Chong,Yu, Fanghua,et al. NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results[C]:IEEE Computer Society,2023:1731-1745. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。