Optimize Preview Model Parameters Evaluation of RANSAC

The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for optimizing the preview model parameters evaluation of RANSAC that has the benefit of offering fast and accurate RANSAC. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. And use local optimization step apply to selected models. The combination of these two strategies results in a robust estimation procedure that provides a significant speed and accuracy RANSAC techniques, while requiring no prior information to guide the sampling process.