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Global Structure-from-Motion Revisited

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this problem are categorized into incremental and global approaches. Until now, the most popular systems follow the incremental paradigm due to its superior accuracy and robustness, while global approaches are drastically more scalable and efficient. With this work, we revisit the problem of global SfM and propose GLOMAP as a new general-purpose system that outperforms the state of the art in global SfM. In terms of accuracy and robustness, we achieve results on-par or superior to COLMAP, the most widely used incremental SfM, while being orders of magnitude faster. We share our system as an open-source implementation at https://github.com/colmap/glomap.

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Acknowledgment

The authors thank Philipp Lindenberger for the thoughtful discussions and comments on the text. This work was partially funded by the Hasler Stiftung Research Grant via the ETH Zurich Foundation and the ETH Zurich Career Seed Award. Linfei Pan was supported by gift funding from Microsoft.

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Authors and Affiliations

  1. ETH Zurich, Zürich, Switzerland

    Linfei Pan, Dániel Baráth & Marc Pollefeys

  2. Microsoft, Redmond, USA

    Marc Pollefeys & Johannes L. Schönberger

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  1. Linfei Pan
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  2. Dániel Baráth
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  4. Johannes L. Schönberger
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Correspondence to Linfei Pan .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Pan, L., Baráth, D., Pollefeys, M., Schönberger, J.L. (2025). Global Structure-from-Motion Revisited. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15098. Springer, Cham. https://doi.org/10.1007/978-3-031-73661-2_4

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