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.

通过优化序列的几何任务，我们提出了一种称为 PRAGO 的求解相机位姿的方法，该方法通过在无序图像上进行目标位置的细化来优化旋转和绝对位置，实现了在小型稀疏场景中相比于非可微求解器更好的性能。