Bundle adjustment (BA) is the standard way to optimise camera poses and to produce sparse representations of a scene. However, as the number of camera poses and features grows, refinement through bundle adjustment becomes inefficient. Inspired by global motion averaging methods, we propose a new bundle adjustment objective which does not rely on image features' reprojection errors yet maintains precision on par with classical BA. Our method averages over relative motions while implicitly incorporating the contribution of the structure in the adjustment. To that end, we weight the objective function by local hessian matrices - a by-product of local bundle adjustments performed on relative motions (e.g., pairs or triplets) during the pose initialisation step. Such hessians are extremely rich as they encapsulate both the features' random errors and the geometric configuration between the cameras. These pieces of information propagated to the global frame help to guide the final optimisation in a more rigorous way. We argue that this approach is an upgraded version of the motion averaging approach and demonstrate its effectiveness on both photogrammetric datasets and computer vision benchmarks.

翻译：集束调整（BA）是优化相机姿态并生成场景稀疏表示的标准方法。然而，随着相机姿态和特征点数量的增加，通过集束调整进行优化的效率会显著降低。受全局运动平均方法的启发，我们提出了一种新的集束调整目标函数，该函数不依赖图像特征的重投影误差，却能保持与传统BA相当的精度。我们的方法在平均相对运动的同时，隐式地将场景结构的影响纳入调整过程。为此，我们采用局部Hessian矩阵对目标函数进行加权——这些矩阵是在姿态初始化阶段对相对运动（如双视图或三视图）执行局部集束调整时产生的副产品。此类Hessian矩阵包含极为丰富的信息，既融合了特征点的随机误差，又反映了相机间的几何构型。将这些信息传播至全局框架，有助于以更严谨的方式指导最终优化。我们证明该方法是运动平均方法的升级版本，并在摄影测量数据集和计算机视觉基准测试中验证了其有效性。