SG-SRNs: Superpixel-Guided Scene Representation Networks | |
Liu, Qiang1,6; Lu, Xiao2; Dong, Qiulei3,4,5; Zhang, Yangyong1,6; Wang, Haixia1,6 | |
刊名 | IEEE SIGNAL PROCESSING LETTERS |
2022 | |
卷号 | 29页码:2038-2042 |
关键词 | Image segmentation Three-dimensional displays Task analysis Image color analysis Image reconstruction Distortion Cameras Scene representation networks self-supervised multi-task learning superpixel-guided superpixel regularization |
ISSN号 | 1070-9908 |
DOI | 10.1109/LSP.2022.3209147 |
通讯作者 | Wang, Haixia(hxwang@sdust.edu.cn) |
英文摘要 | Recently, Scene Representation Networks (SRNs) have attracted increasing attention in computer vision, due to their continuous and light-weight scene representation ability. However, SRNs generally perform poorly on low-texture image regions. Addressing this problem, we propose superpixel-guided scene representation networks in this paper, called SG-SRNs, consisting of a backbone module (SRNs), a superpixel segmentation module, and a superpixel regularization module. In the proposed method, except for the novel view synthesis task, the task of representation-aware superpixel segmentation mask generation is realized by the proposed superpixel segmentation module. Then, the superpixel regularization module utilizes the superpixel segmentation mask to guide the backbone to be learned in a locally smooth way, and optimizes the scene representations of the local regions to indirectly alleviate the structure distortion of low-texture regions in a self-supervised manner. Extensive experimental results on both our constructed datasets and the public Synthetic-NeRF dataset demonstrated that the proposed SG-SRNs achieved a significantly better 3D structure representing performance. |
资助项目 | National Natural Science Foundation of China[62073199] ; National Natural Science Foundation of China[62273213] ; National Natural Science Foundation of China[U1805264] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100] ; Beijing Municipal Science and Technology Project[Z211100011021004] ; Natural Science Foundation of Shandong Province[ZR2020MF095] ; Taishan Scholarship Construction Engineering |
WOS研究方向 | Engineering |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000866498400005 |
资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Municipal Science and Technology Project ; Natural Science Foundation of Shandong Province ; Taishan Scholarship Construction Engineering |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/50334] |
专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
通讯作者 | Wang, Haixia |
作者单位 | 1.Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China 2.Shandong Univ Sci & Technol, Coll Energy Storage Technol, Qingdao 266590, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 6.Shandong Univ Sci & Technol, Key Lab Robot & Intelligent Technol Shandong Prov, Qingdao 266590, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Qiang,Lu, Xiao,Dong, Qiulei,et al. SG-SRNs: Superpixel-Guided Scene Representation Networks[J]. IEEE SIGNAL PROCESSING LETTERS,2022,29:2038-2042. |
APA | Liu, Qiang,Lu, Xiao,Dong, Qiulei,Zhang, Yangyong,&Wang, Haixia.(2022).SG-SRNs: Superpixel-Guided Scene Representation Networks.IEEE SIGNAL PROCESSING LETTERS,29,2038-2042. |
MLA | Liu, Qiang,et al."SG-SRNs: Superpixel-Guided Scene Representation Networks".IEEE SIGNAL PROCESSING LETTERS 29(2022):2038-2042. |
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