Extracting planar structures efficiently with revisited BetaSAC

We present a new method for the detection of multiple homographies in image pairs. Our aim is to show that we can approach the optimal solution in a short time using an approach based on the well-known RANSAC algorithm. Given feature correspondences between two similar images, our algorithm iteratively generates homography hypotheses using a suitable sampling, optimizes the promising hypotheses and combine them to keep the best models. The performance of the method is demonstrated on synthetic and real data and we show that it outperforms the J-linkage technique for the multiple homographies estimation problem.