Learning to Solve Hard Minimal Problems

Understand

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.