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Distributed sparse bundle adjustment algorithm based on three-dimensional point partition and asynchronous communication

  • Xiao-long Shen
  • Yong Dou
  • Steven Mills
  • David M Eyers
  • Huan Feng
  • Zhiyi Huang
Article
  • 2 Downloads

Abstract

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).

Key words

Sparse bundle adjustment Parallel Distributed sparse bundle adjustment Three-dimensional reconstruction Asynchronous 

CLC number

TP312 TP217.4 

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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Science and Technology on Parallel and Distributed LaboratoryNational University of Defense TechnologyChangshaChina
  3. 3.Department of Computer ScienceUniversity of OtagoDunedinNew Zealand
  4. 4.Department of Computer ScienceTsinghua UniversityBeijingChina

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