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Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12360))

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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.

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Notes

  1. 1.

    Since one of the image sets Union Square has only 10 reconstructed images, we replace it with another public image set ArtsQuad.

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Acknowledgement

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

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Lei Zhou, Zixin Luo, Mingmin Zhen, Tianwei Shen, Zhuofei Huang & Long Quan

  2. Everest Innovation Technology, Hong Kong, China

    Shiwei Li & Tian Fang

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  1. Lei Zhou
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  2. Zixin Luo
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Correspondence to Lei Zhou .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Zhou, L. et al. (2020). Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-58555-6_22

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  • Online ISBN: 978-3-030-58555-6

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