Fast correspondence-based point cloud registration by pair-wise inlier checking and transformation decomposition

Abstract

Abstract Correspondence-based point cloud registration is widely used in the computer vision field and it is often solved by RANSAC, which is stable and fast when the outlier ratio is moderate or low. However, RANSAC will be very slow when the outlier ratio is extremely high, such as 99% or even higher, which is not rare especially when there are many repeated structures or the structure diversity is low in the scene. Outlier removal method was developed as a preprocessing step to improve the efficiency in very high outlier ratio, but its efficiency goes low when the outlier ratio goes down, such as lower than 95%. In this paper, we address this problem from another perspective. Instead of removing outliers, we select potential inliers by checking the consistency of distance of a pair of correspondences and estimate translation and rotation separately by transformation decomposition using the selected correspondence pairs. Experiments on both synthetic and real data show that the proposed method is fast in both high and low outlier ratios and it is significantly more efficient than RANSAC and outlier removal approach when the outlier ratio is extremely high.