  Random sample consensus (RANSAC) is a robust model-fitting algorithm. It is
widely used in many fields including image-stitching and point cloud
registration. In RANSAC, data is uniformly sampled for hypothesis generation.
However, this uniform sampling strategy does not fully utilize all the
information on many problems. In this paper, we propose a method that samples
data with a L\'{e}vy distribution together with a data sorting algorithm. In
the hypothesis sampling step of the proposed method, data is sorted with a
sorting algorithm we proposed, which sorts data based on the likelihood of a
data point being in the inlier set. Then, hypotheses are sampled from the
sorted data with L\'{e}vy distribution. The proposed method is evaluated on
both simulation and real-world public datasets. Our method shows better results
compared with the uniform baseline method.
