Robust fitting for multiple view geometry

TL;DR: It is shown that for most fitting problems, a solution can be found with an algorithm that has polynomial time-complexity in the number of points (independent of the rate of outliers), and other cost functions can also be handled within the same framework with the same time complexity.

Abstract: How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the number of outliers can be found with an algorithm that has polynomial time-complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L2-norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required conditions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.