  RANSAC-based algorithms are the standard techniques for robust estimation in
computer vision. These algorithms are iterative and computationally expensive;
they alternate between random sampling of data, computing hypotheses, and
running inlier counting. Many authors tried different approaches to improve
efficiency. One of the major improvements is having a guided sampling, letting
the RANSAC cycle stop sooner. This paper presents a new adaptive sampling
process for RANSAC. Previous methods either assume no prior information about
the inlier/outlier classification of data points or use some previously
computed scores in the sampling. In this paper, we derive a dynamic Bayesian
network that updates individual data points' inlier scores while iterating
RANSAC. At each iteration, we apply weighted sampling using the updated scores.
Our method works with or without prior data point scorings. In addition, we use
the updated inlier/outlier scoring for deriving a new stopping criterion for
the RANSAC loop. We test our method in multiple real-world datasets for several
applications and obtain state-of-the-art results. Our method outperforms the
baselines in accuracy while needing less computational time.
