RPBA --基于协方差信息的稳健并行光束法平差
RPBA -- Robust Parallel Bundle Adjustment Based on Covariance Information
摘要：
所有运动恢复结构（SfM）方法的核心组成部分是光束法平差。由于后者是较大块的计算瓶颈，因此并行光束法平差已成为一个活跃的研究领域。特别是，基于共识的优化方法已被证明是适合这个任务。我们已经使用通过调整各个三维（3D）点而导出的协方差信息来扩展它们，即，“三角测量”或“相交”。这不仅会导致更好的收敛行为，而且还避免了对标准共识方法的惩罚参数进行微调。相应的新方法也可以被看作是切除/相交方案的变体，其中我们在相交期间调整与计算机上可用线程的数量直接相关的子块的数量，每个子块包含块的相机的一部分。我们表明，我们的新方法是适合强大的并行光束法平差，并证明其能力相比，基本的共识为基础的方法，以及一个国家的最先进的并行实现光束法平差。我们的新方法的代码可以在GitHub上找到：https://github.com/helmayer/RPBA
英文摘要：
A core component of all Structure from Motion (SfM) approaches is bundle adjustment. As the latter is a computational bottleneck for larger blocks, parallel bundle adjustment has become an active area of research. Particularly, consensus-based optimization methods have been shown to be suitable for this task. We have extended them using covariance information derived by the adjustment of individual three-dimensional (3D) points, i.e., "triangulation" or "intersection". This does not only lead to a much better convergence behavior, but also avoids fiddling with the penalty parameter of standard consensus-based approaches. The corresponding novel approach can also be seen as a variant of resection / intersection schemes, where we adjust during intersection a number of sub-blocks directly related to the number of threads available on a computer each containing a fraction of the cameras of the block. We show that our novel approach is suitable for robust parallel bundle adjustment and demonstrate its capabilities in comparison to the basic consensus-based approach as well as a state-of-the-art parallel implementation of bundle adjustment. Code for our novel approach is available on GitHub: https://github.com/helmayer/RPBA
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
