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Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360)

Abstract

Current bundle adjustment solvers such as the Levenberg-Marquardt (LM) algorithm are limited by the bottleneck in solving the Reduced Camera System (RCS) whose dimension is proportional to the camera number. When the problem is scaled up, this step is neither efficient in computation nor manageable for a single compute node. In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability. It first reformulates the quadratic programming problem of an LM iteration based on the clustering of the visibility graph by introducing the equality constraints across clusters. Then, we propose to relax it into a chance constrained problem and solve it through sampled convex program. The relaxation is intended to eliminate the interdependence between clusters embodied by the constraints, so that a large RCS can be decomposed into independent linear sub-problems. Numerical experiments on unordered Internet image sets and sequential SLAM image sets, as well as distributed experiments on large-scale datasets, have demonstrated the high efficiency and scalability of the proposed approach. Codes are released at https://github.com/zlthinker/STBA.

Keywords

Stochastic bundle adjustment Clustering 3D reconstruction 

Notes

Acknowledgement

This work is supported by Hong Kong RGC GRF16206819 & 16203518 and T22-603/15N.

Supplementary material

504470_1_En_22_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (pdf 1899 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Hong Kong University of Science and TechnologyHong KongChina
  2. 2.Everest Innovation TechnologyHong KongChina

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