Large-Scale Semantic Scene Understanding with Cross-Correction Representation

doi:10.3390/rs14236022

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 三维可视计算

	Large-Scale Semantic Scene Understanding with Cross-Correction Representation
	Zhao, Yuehua 1; Zhang, Jiguang2 ; Ma, Jie 1; Xu, Shibiao3
发表期刊	REMOTE SENSING
	2022-12-01
卷号	14 期号:23 页码:15
通讯作者	Ma, Jie(jma@hebut.edu.cn) ; Xu, Shibiao(shibiaoxu@bupt.edu.cn)
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.
关键词	point cloud large-scale semantic segmentation spatial geometric semantic context cross-correction
DOI	10.3390/rs14236022
关键词[WOS]	POINT ; SEGMENTATION ; NETWORKS
收录类别	SCI
语种	英语
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
项目资助者	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
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000897456900001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51309
专题	多模态人工智能系统全国重点实验室_三维可视计算
通讯作者	Ma, Jie; Xu, Shibiao
作者单位	1.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300401, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100090, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, 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.