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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. 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 Ceres and 174.6x over DeepLM.
Code: https://github.com/facebookresearch/DABA.
