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Effective piecewise planar modeling based on sparse 3D points and convolutional neural network

文献类型：期刊论文

作者Wang, Wei2; Gao, Wei1; Hu, Zhanyi1
刊名NEUROCOMPUTING
出版日期2020-02-22
卷号378页码:350-363
关键词Urban scene Piecewise planar stereo Markov Random Field Image over-segmentation Convolutional neural network
ISSN号0925-2312
DOI10.1016/j.neucom.2019.10.026
通讯作者Wang, Wei(wangwei@zknu.cn)
英文摘要Piecewise planar stereo methods can approximately reconstruct the complete structures of a scene by overcoming challenging difficulties (e.g., poorly textured regions) that pixel-level stereo methods cannot resolve. In this paper, a novel plane assignment cost is first constructed by incorporating scene structure priors and high-level image features obtained by convolutional neural network (CNN). Then, the piecewise planar scene structures are reconstructed in a progressive manner that jointly optimizes image regions (or superpixels) and their associated planes, followed by a global plane assignment optimization under a Markov Random Field (MRF) framework. Experimental results on a variety of urban scenes confirm that the proposed method can effectively reconstruct the complete structures of a scene from only sparse three-dimensional (3D) points with high efficiency and accuracy and can achieve superior results compared with state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
WOS关键词SUPERPIXELS
资助项目National Key R&D Program of China[2016YFB0502002] ; National Natural Science Foundation of China[U1805264] ; National Natural Science Foundation of China[617724 44] ; National Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61873264] ; Natural Science Foundation of Henan Province[162300410347] ; Key Scientific and Technological Project of Henan Province[162102310589] ; Key Scientific and Technological Project of Henan Province[192102210279]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000506202200031
出版者ELSEVIER
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Henan Province ; Key Scientific and Technological Project of Henan Province
源URL[http://ir.ia.ac.cn/handle/173211/29447]  
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, Wei
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Zhoukou Normal Univ, Sch Network Engn, Zhoukou 466001, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Wei,Gao, Wei,Hu, Zhanyi. Effective piecewise planar modeling based on sparse 3D points and convolutional neural network[J]. NEUROCOMPUTING,2020,378:350-363.
APA Wang, Wei,Gao, Wei,&Hu, Zhanyi.(2020).Effective piecewise planar modeling based on sparse 3D points and convolutional neural network.NEUROCOMPUTING,378,350-363.
MLA Wang, Wei,et al."Effective piecewise planar modeling based on sparse 3D points and convolutional neural network".NEUROCOMPUTING 378(2020):350-363.

入库方式： OAI收割

来源：自动化研究所

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