

FOLLOWUS
College of Computer, National University of Defense Technology, Changsha 410000, China
Science and Technology on Parallel and Distributed Laboratory, National University of Defense Technology, Changsha 410000, China
Department of Computer Science, University of Otago, Dunedin 9016, New Zealand
Department of Computer Science, Tsinghua University, Beijing 100084, China
Xiao-long SHEN, E-mail: shenxiaolong11@nudt.edu.cn
[ "Yong DOU, E-mail:yongdou@nudt.edu.cn" ]
[ "Steven MILLS, E-mail:steven@cs.otago.ac.nz" ]
[ "David M EYERS, E-mail: dme@cs.otago.ac.nz" ]
[ "Huan FENG, E-mail:fenghuan517@gmail.com" ]
[ "Zhiyi HUANG, E-mail:zhuang@cs.otago.ac.nz" ]
Received:20 March 2018,
Revised:2018-;7-13,
Published:2018-07
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Xiao-long SHEN, Yong DOU, Steven MILLS, et al. Distributed sparse bundle adjustment algorithm based on three-dimensional point partition and asynchronous communication[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 889-904.
Xiao-long SHEN, Yong DOU, Steven MILLS, et al. Distributed sparse bundle adjustment algorithm based on three-dimensional point partition and asynchronous communication[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 889-904. DOI: 10.1631/FITEE.1800173.
稀疏捆绑调整(sparse bundle adjustment,SBA)是三维重建的关键步骤,但其速度慢且内存需求高。提出一种基于三维点的分布式SBA算法,以提高SBA速度和可扩展性。该算法利用异步约简通信机制(A-DSBA),将数据通信与方程组求解重叠。与同步DSBA(S-DSBA)相比,A-DSBA运行时间缩短46%。对几个三维重建数据集的实验结果表明,在8个节点上运行的分布式算法比独立并行SBA快5倍。此外,该算法在串行SBA(在单个节点上运行)上的加速比达到41。
Sparse bundle adjustment (SBA) is a key but time- and memory-consuming step in three-dimensional (3D) reconstruction. In this paper
we propose a 3D point-based distributed SBA algorithm (DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment (A-DSBA) to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism (SDSBA)
A-DSBA reduces the running time by 46%. The experimental results on several 3D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore
the speedup of the proposed algorithm (running on eight nodes with 48 cores) is up to 41 times that of the serial SBA (running on a single node).
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