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

BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus