We present an approach to solving hard geometric optimization problems in the
RANSAC framework. The hard minimal problems arise from relaxing the original
geometric optimization problem into a minimal problem with many spurious
solutions. Our approach avoids computing large numbers of spurious solutions.
We design a learning strategy for selecting a starting problem-solution pair
that can be numerically continued to the problem and the solution of interest.
We demonstrate our approach by developing a RANSAC solver for the problem of
computing the relative pose of three calibrated cameras, via a minimal
relaxation using four points in each view. On average, we can solve a single
problem in under 70 $\mu s.$ We also benchmark and study our engineering
choices on the very familiar problem of computing the relative pose of two
calibrated cameras, via the minimal case of five points in two views.

该研究提出了一种基于 RANSAC 框架求解几何优化问题的方法，通过设计一种学习策略，可以避免计算大量伪解，从而有效地解决了几何优化问题的难点。通过在相对位姿问题中使用该方法，在每个视图中使用四个点进行最小松弛，可以快速精确地计算出相机之间的相对位置。