DABA: Decentralized and accelerated large-scale bundle adjustment

Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices. Centralized methods in prior works are only able to solve small or medium size problems due to overhead in computation and communication. In this paper, we present a fully decentralized method that alleviates computation and communication bottlenecks to solve arbitrarily large bundle adjustment problems. We achieve this by reformulating the reprojection error and deriving a novel surrogate function that decouples optimization variables from different devices. This function makes it possible to use majorization minimization techniques and reduces bundle adjustment to independent optimization subproblems that can be solved in parallel. Moreover, an efficient closed-form warm start strategy has been presented that always improves bundle adjustment estimates. We further apply Nesterov’s acceleration and adaptive restart to improve convergence while maintaining its theoretical guarantees. Despite limited peer-to-peer communication, our method has provable convergence to first-order critical points under mild conditions. On extensive benchmarks with public datasets, our method converges much faster than decentralized baselines with similar memory usage and communication load. Compared to centralized baselines using a single device, our method, while being decentralized, yields more accurate solutions with significant speedups of up to 953.7x over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="sans-serif">C</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">r</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">s</mml:mi> </mml:math> and 174.6x over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="sans-serif">D</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">p</mml:mi> <mml:mi mathvariant="sans-serif">L</mml:mi> <mml:mi mathvariant="sans-serif">M</mml:mi> </mml:math> . Code: https://github.com/facebookresearch/ DABA .

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Publication Details

Journal
The International Journal of Robotics Research
Published
2025-02-05
DOI
https://doi.org/10.1177/02783649241309968
Citations
1
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
4.99

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article

DABA: Decentralized and accelerated large-scale bundle adjustment

Maurizio Monge, Taosha Fan, Mustafa Mukadam, Todd D. Murphey et al.
1 citations
The International Journal of Robotics Research
Robotics and Sensor-Based Localization
4.99
article

DABA: Decentralized and accelerated large-scale bundle adjustment

Maurizio Monge, Taosha Fan, Mustafa Mukadam, Todd D. Murphey, Joseph D. Ortiz, Ming Hsiao, Jing Dong
article en
1 citations

Abstract

Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices. Centralized methods in prior works are only able to solve small or medium size problems due to overhead in computation and communication. In this paper, we present a fully decentralized method that alleviates computation and communication bottlenecks to solve arbitrarily large bundle adjustment problems. We achieve this by reformulating the reprojection error and deriving a novel surrogate function that decouples optimization variables from different devices. This function makes it possible to use majorization minimization techniques and reduces bundle adjustment to independent optimization subproblems that can be solved in parallel. Moreover, an efficient closed-form warm start strategy has been presented that always improves bundle adjustment estimates. We further apply Nesterov’s acceleration and adaptive restart to improve convergence while maintaining its theoretical guarantees. Despite limited peer-to-peer communication, our method has provable convergence to first-order critical points under mild conditions. On extensive benchmarks with public datasets, our method converges much faster than decentralized baselines with similar memory usage and communication load. Compared to centralized baselines using a single device, our method, while being decentralized, yields more accurate solutions with significant speedups of up to 953.7x over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="sans-serif">C</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">r</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">s</mml:mi> </mml:math> and 174.6x over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="sans-serif">D</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">e</mml:mi> <mml:mi mathvariant="sans-serif">p</mml:mi> <mml:mi mathvariant="sans-serif">L</mml:mi> <mml:mi mathvariant="sans-serif">M</mml:mi> </mml:math> . Code: https://github.com/facebookresearch/ DABA .

The International Journal of Robotics ResearchVol. 44(10-11)
Northwestern University (US), University of Pittsburgh (US), Facility for Antiproton and Ion Research (DE)
National Science Foundation
Openalex Percentile: Top 7%
Robotics and Sensor-Based Localization
4.99
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