Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration

doi:10.1016/j.neucom.2020.06.102

CORC > 兰州理工大学 > 兰州理工大学

	Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration
	Li, Zhongyu 3; Liu, Jiamin 3; Tian, Zhiqiang 3; Zhu, Jihua 3; Li, Ce 2; Du, Shaoyi 1
刊名	Neurocomputing
	2020-11-06
卷号	413 页码:230-239
关键词	Image reconstruction Lagrange multipliers 3D scene reconstruction Adaptive weights Averaging method Lagrange multiplier method Matrix decomposition Multi-view registration Optimization strategy State of the art
ISSN号	09252312
DOI	10.1016/j.neucom.2020.06.102
英文摘要	Motion averaging has recently been introduced as an effective means to tackle the registration of multi-view range scans. This approach can view parts of pair-wise motions with high reliability as an input to estimate the global motions for a multi-view registration. However, reliable pair-wise motions are not easy to confirm in most practical applications without prior knowledge. In this paper, we propose an adaptive low-rank sparse (LRS) weighted motion averaging method for a robust and accurate multi-view registration, which can directly reconstruct high-quality 3D shape models from a set of unordered range scans. Specifically, we first introduce LRS matrix decomposition to automatically compute the initial global motions. The LRS matrix decomposition can recover the initial global models through the full exploration of a set of pair-wise motions. Subsequently, we extend the motion averaging with an adaptive weight computation by developing an optimization strategy using the Lagrange multiplier method, which can adaptively compute the weights of the reliability for each pair-wise relative motion. Accordingly, the proposed method can recover accurate and robust global motions in a set of iterations through weighted motion averaging. Experimental results on several public datasets demonstrate the excellent performance of the proposed method in comparison with state-of-the-art multi-view registration and 3D scene reconstruction. © 2020 Elsevier B.V.
WOS研究方向	Computer Science
语种	英语
出版者	Elsevier B.V.
WOS记录号	WOS:000579803700020
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/115468]
专题	兰州理工大学
作者单位	1.Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an; 710049, China 2.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.School of Software Engineering, Xi'an Jiaotong University, Xi'an, China;
推荐引用方式 GB/T 7714	Li, Zhongyu,Liu, Jiamin,Tian, Zhiqiang,et al. Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration[J]. Neurocomputing,2020,413:230-239.
APA	Li, Zhongyu,Liu, Jiamin,Tian, Zhiqiang,Zhu, Jihua,Li, Ce,&Du, Shaoyi.(2020).Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration.Neurocomputing,413,230-239.
MLA	Li, Zhongyu,et al."Adaptive weighted motion averaging with low-rank sparse for robust multi-view registration".Neurocomputing 413(2020):230-239.