1-Point Sample Consensus on Correspondence Set for 3D Point Cloud Registration

Estimating a six-degree-of-freedom pose from a set of correspondences remains a popular solution for 3D point cloud registration. The random sample consensus (RANSAC) method is a typical pose estimator for this task. However, RANSAC still suffers from several limitations including low efficiency and the sensitivity to high outlier ratios. To tackle these problems, we propose a 1-point sample consensus method. It first constructs a local reference frame for the keypoint based on multi-scale normal vectors, which allows our method to exhibit a linear time complexity. Then, we propose a novel hypothesis evaluation method that concentrates on accurate inliers and is more reliable for hypothesis evaluation. With comparisons with two RANSAC-like methods, our method manages to achieve more accurate and efficient registrations, making it a good gift for practical applications.