Full Text:   <302>

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CLC number: TP391.41

On-line Access: 2020-10-14

Received: 2019-08-30

Revision Accepted: 2020-01-05

Crosschecked: 2020-06-04

Cited: 0

Clicked: 508

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhuo-hao Liu

https://orcid.org/0000-0001-7093-6267

Chang-yu Diao

https://orcid.org/0000-0001-7744-0889

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.10 P.1442-1454

http://doi.org/10.1631/FITEE.1900451


A low-overhead asynchronous consensus framework for distributed bundle adjustment


Author(s):  Zhuo-hao Liu, Chang-yu Diao, Wei Xing, Dong-ming Lu

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   roadliu@zju.edu.cn, dcy@zju.edu.cn

Key Words:  Structure-from-motion, Distributed bundle adjustment, Overhead, Asynchronous consensus, Partial barrier, Bipartite graph summarization


Zhuo-hao Liu, Chang-yu Diao, Wei Xing, Dong-ming Lu. A low-overhead asynchronous consensus framework for distributed bundle adjustment[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1442-1454.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900451"
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Abstract: 
Generally, the distributed bundle adjustment (DBA) method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer. However, the performance considerably degrades owing to the overhead introduced by the additional block partitioning step and synchronous waiting. Therefore, we propose a low-overhead consensus framework. A partial barrier based asynchronous method is proposed to early achieve consensus with respect to the faster worker nodes to avoid waiting for the slower ones. A scene summarization procedure is designed and integrated into the block partitioning step to ensure that clustering can be performed on the small summarized scene. Experiments conducted on public datasets show that our method can improve the worker node utilization rate and reduce the block partitioning time. Also, sample applications are demonstrated using our large-scale culture heritage datasets.

一种用于分布式集束调整的低开销异步共识框架

刘卓昊1,刁常宇2,3,邢卫1,鲁东明1,3
1浙江大学计算机科学与技术学院,中国杭州市,310027
2浙江大学文化遗产研究院,中国杭州市,310027
3浙江大学石窟寺数字化保护重点科研基地,中国杭州市,310027

摘要:分布式集束调整方法使用多个工作节点解决集束调整问题,克服单台计算机的计算和内存存储限制。但是,额外的块划分步骤和同步等待会引入可观的性能开销。因此,我们提出一个低开销共识框架,该方法基于异步共识融合使先到达的节点先共识融合,避免等待较慢的计算节点。此外,提出一个场景汇总方法,并将其集成到块划分步骤,用以在小规模汇总场景上执行聚类。在公开数据集上的实验结果表明,本文方法可以提高工作节点利用率,减少块划分时间。此外,在大规模文化遗产数据集上的实验也证明该方法有效。

关键词:运动恢复机构;分布式集束调整;计算开销;异步共识;部分屏障;二部图汇总

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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