Robustly estimating camera poses from a set of images is a fundamental task which remains challenging for differentiable methods, especially in the case of small and sparse camera pose graphs. To overcome this challenge, we propose Pose-refined Rotation Averaging Graph Optimization (PRAGO). From a set of objectness detections on unordered images, our method reconstructs the rotational pose, and in turn, the absolute pose, in a differentiable manner benefiting from the optimization of a sequence of geometrical tasks. We show how our objectness pose-refinement module in PRAGO is able to refine the inherent ambiguities in pairwise relative pose estimation without removing edges and avoiding making early decisions on the viability of graph edges. PRAGO then refines the absolute rotations through iterative graph construction, reweighting the graph edges to compute the final rotational pose, which can be converted into absolute poses using translation averaging. We show that PRAGO is able to outperform non-differentiable solvers on small and sparse scenes extracted from 7-Scenes achieving a relative improvement of 21% for rotations while achieving similar translation estimates.

翻译：从一组图像中稳健估计相机位姿是一项基础任务，但可微方法仍面临挑战，尤其是在相机位姿图规模小且稀疏的情况下。为克服这一挑战，我们提出了位姿精化旋转平均图优化（PRAGO）方法。基于无序图像上的目标性检测，该方法以可微方式重建旋转位姿，进而获得绝对位姿，并通过一系列几何任务的优化获得显著效果。我们展示了PRAGO中的目标性位姿精化模块如何在不删除边且避免对图边可行性做出早期决策的前提下，精化成对相对位姿估计中的固有歧义。随后，PRAGO通过迭代图构建精化绝对旋转，重新加权图边以计算最终旋转位姿，并可通过平移平均将其转换为绝对位姿。实验表明，在从7-Scenes数据集中提取的小规模稀疏场景下，PRAGO在旋转估计上实现了21%的相对提升，同时平移估计精度与不可微求解器相当。