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Large-Scale Semantic Scene Understanding with Cross-Correction Representation

文献类型：期刊论文

作者Zhao, Yuehua3; Zhang, Jiguang2; Ma, Jie3; Xu, Shibiao1
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:23页码:15
关键词point cloud large-scale semantic segmentation spatial geometric semantic context cross-correction
DOI10.3390/rs14236022
通讯作者Ma, Jie(jma@hebut.edu.cn) ; Xu, Shibiao(shibiaoxu@bupt.edu.cn)
英文摘要Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. However, most of them only exploit local spatial geometric or semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial-Semantic Incorporation Network (SSI-Net) for real-time large-scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High-quality contextual features can be learned through SSC by correcting and updating high-level semantic information using spatial geometric cues and vice versa. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder-decoder architecture. To ensure efficiency, it also adopts a random sample-based hierarchical network structure. Extensive experiments on several prevalent indoor and outdoor datasets for point cloud semantic segmentation demonstrate that the proposed approach can achieve state-of-the-art performance.
WOS关键词POINT ; SEGMENTATION ; NETWORKS
资助项目Hebei Natural Science Foundation[F2020202045] ; National Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[62271074] ; National Natural Science Foundation of China[61972459] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62071157] ; National Natural Science Foundation of China[62162044] ; National Natural Science Foundation of China[32271983] ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences[LSU-KFJJ-2021-05] ; Open Projects Program of National Laboratory of Pattern Recognition
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000897456900001
出版者MDPI
资助机构Hebei Natural Science Foundation ; National Natural Science Foundation of China ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences ; Open Projects Program of National Laboratory of Pattern Recognition
源URL[http://ir.ia.ac.cn/handle/173211/51308]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Ma, Jie; Xu, Shibiao
作者单位1.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100090, Peoples R China
3.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300401, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Yuehua,Zhang, Jiguang,Ma, Jie,et al. Large-Scale Semantic Scene Understanding with Cross-Correction Representation[J]. REMOTE SENSING,2022,14(23):15.
APA Zhao, Yuehua,Zhang, Jiguang,Ma, Jie,&Xu, Shibiao.(2022).Large-Scale Semantic Scene Understanding with Cross-Correction Representation.REMOTE SENSING,14(23),15.
MLA Zhao, Yuehua,et al."Large-Scale Semantic Scene Understanding with Cross-Correction Representation".REMOTE SENSING 14.23(2022):15.

入库方式： OAI收割

来源：自动化研究所

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