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：我们提出一种基于精确列文伯格-马夸尔特算法的分布式集束调整方法，适用于超大规模数据集。现有方法大多将全局地图划分为子地图并在子地图内进行集束调整。为适配并行框架，这些方法采用近似解替代LM算法，但往往导致次优结果。与此不同，我们利用精确LM算法执行全局集束调整，其中简化相机系统的构建过程以分布式方式并行执行。为存储大型简化相机系统，我们采用基于块的稀疏矩阵压缩格式（BSMC）对其进行压缩，该格式充分利用了其分块特征。BSMC格式还支持全局简化相机系统的分布式存储与更新。通过合成数据集与真实数据集，我们将所提方法与现有最优管线进行广泛评估与对比。初步结果表明，与基线方法相比，本方法在内存使用效率与可扩展性方面表现优异。我们首次在包含118万张图像的真实数据集和包含1000万张图像（约为现有最优基于LM的集束调整规模500倍）的合成数据集上，利用分布式计算系统实现了基于LM算法的并行集束调整。