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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\u2019s 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\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi mathvariant=\"sans-serif\">C<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">e<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">r<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">e<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">s<\/mml:mi>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    and 174.6x over\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi mathvariant=\"sans-serif\">D<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">e<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">e<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">p<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">L<\/mml:mi>\n                        <mml:mi mathvariant=\"sans-serif\">M<\/mml:mi>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    . 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