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We propose a distributed bundle adjustment (DBA) method using the exactLevenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of theexisting methods partition the global map to small ones and conduct bundleadjustment in the submaps. In order to fit the parallel framework, they useapproximate solutions instead of the LM algorithm. However, those methods oftengive sub-optimal results. Different from them, we utilize the exact LMalgorithm to conduct global bundle adjustment where the formation of thereduced camera system (RCS) is actually parallelized and executed in adistributed way. To store the large RCS, we compress it with a block-basedsparse matrix compression format (BSMC), which fully exploits its blockfeature. The BSMC format also enables the distributed storage and updating ofthe global RCS. The proposed method is extensively evaluated and compared withthe state-of-the-art pipelines using both synthetic and real datasets.Preliminary results demonstrate the efficient memory usage and vast scalabilityof the proposed method compared with the baselines. For the first time, weconducted parallel bundle adjustment using LM algorithm on a real datasets with1.18 million images and a synthetic dataset with 10 million images (about 500times that of the state-of-the-art LM-based BA) on a distributed computingsystem.

Distributed bundle adjustment with block-based sparse matrix compression for super large scale datasets