We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct bundle adjustment in the submaps. In order to fit the parallel framework, they use approximate solutions instead of the LM algorithm. However, those methods often give sub-optimal results. Different from them, we utilize the exact LM algorithm to conduct global bundle adjustment where the formation of the reduced camera system (RCS) is actually parallelized and executed in a distributed way. To store the large RCS, we compress it with a block-based sparse matrix compression format (BSMC), which fully exploits its block feature. The BSMC format also enables the distributed storage and updating of the global RCS. The proposed method is extensively evaluated and compared with the state-of-the-art pipelines using both synthetic and real datasets. Preliminary results demonstrate the efficient memory usage and vast scalability of the proposed method compared with the baselines. For the first time, we conducted parallel bundle adjustment using LM algorithm on a real datasets with 1.18 million images and a synthetic dataset with 10 million images (about 500 times that of the state-of-the-art LM-based BA) on a distributed computing system.

翻译：我们提出了一种采用精确莱文贝格-马夸尔特（LM）算法的分布式光束平差（DBA）方法，适用于超大规模数据集。现有方法大多将全局地图划分为若干子图，并在子图内执行光束平差。为适配并行框架，这些方法常使用近似解替代LM算法，但往往得到次优结果。与此不同，我们利用精确LM算法进行全局光束平差，其中简化相机系统（RCS）的构建以分布式方式并行执行。为存储大规模RCS，我们采用基于块的稀疏矩阵压缩格式（BSMC），该格式充分利用其块结构特征，同时支持全局RCS的分布式存储与更新。通过合成数据集与真实数据集，我们将所提方法与当前最优流程进行了全面评估与比较。初步结果表明，相较基线方法，本方法具有高效的内存使用和强大的可扩展性。我们首次在分布式计算系统上，使用LM算法对包含118万张图像的真实数据集和包含1000万张图像（约是当前基于LM的最优BA方法的500倍）的合成数据集完成了并行光束平差。