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。

学习解决困难极小化问题

Learning to Solve Hard Minimal Problems

In this work we propose an approach for estimating 3D human poses of multiple
people from a set of calibrated cameras. Estimating 3D human poses from
multiple views has several compelling properties: human poses are estimated
within a global coordinate space and multiple cameras provide an extended field
of view which helps in resolving ambiguities, occlusions and motion blur. Our
approach builds upon a real-time 2D multi-person pose estimation system and
greedily solves the association problem between multiple views. We utilize
bipartite matching to track multiple people over multiple frames. This proofs
to be especially efficient as problems associated with greedy matching such as
occlusion can be easily resolved in 3D. Our approach achieves state-of-the-art
results on popular benchmarks and may serve as a baseline for future work.

本研究提出了一种从校准相机组中估计多个人的 3D 人体姿态的方法，通过多个视角估计人体姿态在全局坐标系内，并利用二分图匹配跟踪多人在多帧中实现特别高效的 3D 解决方法，取得了最先进的结果。