In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries.We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://github.com/zhangganlin/GlobalSfMpy

翻译：本文重新审视了全局式运动恢复结构流程中应用的旋转平均问题。我们认为当前方法的主要问题在于最小化代价函数与输入数据之间仅通过估计的对极几何形成弱关联。我们提出通过将点对应关系中的不确定性直接传播至旋转平均过程，从而更精确地建模潜在噪声分布。这种不确定性可通过考虑双视图优化的雅可比矩阵免费获取。此外，我们探索将MAGSAC损失函数的变体集成至旋转平均问题，以替代当前框架中使用的经典鲁棒损失函数。所提方法在大型公共基准测试上相较于基线方法取得了更优的精度。代码已开源。https://github.com/zhangganlin/GlobalSfMpy