BOTTERILL, MILLS, GREEN: CONDITIONAL SAMPLING STRATEGIES FOR RANSAC 1 New Conditional Sampling Strategies for Speeded-Up RANSAC

RANSAC (Random Sample Consensus) is a popular algorithm in computer vision for fitting a model to data points contaminated with many gross outliers. Traditionally many small hypothesis sets are chosen randomly; these are used to generate models and the model consistent with most data points is selected. Instead we propose that each hypothesis set chosen is the one most likely to be correct, conditional on the knowledge of those that have failed to lead to a good model. We present two algorithms, BaySAC and SimSAC, to choose this most likely hypothesis set. We show them to outperform previous improved sampling methods on both real and synthetic data, sometimes halving the number of iterations required. In the case of real-time essential matrix estimation, BaySAC can reduce the failure rate by 78 % with negligible additional cost.