SPROSAC: Streamlined progressive sample consensus for coarse–fine point cloud registration

With the development of 3D matching technology, point cloud registration (PCR) based on corresponding points has received increasing attention in the field of computer vision. Unfortunately, 3D keypoint technology inevitably produces a large number of outliers. To solve the problems of poor stability, low efficiency and the high number of iterations required to calculate the accepted solution of random sampling consistency (RANSAC) and its variants under a high outlier rate, a streamlined progressive sample consensus (SPROSAC) algorithm is proposed in this paper. SPROSAC is an improved estimator of progressive sample consensus that guides the sampling process by increasing the use of 3D point cloud surface information and optimizes the model verification process based on registration error decision acceptance. Compared to classic RANSAC-family algorithms, SPROSAC has a greater probability of obtaining an accepted solution more quickly. The experiments demonstrate that SPROSAC achieves significantly smaller and more stable registration errors with fewer iterations across three datasets. In the performance experiments based on evaluation metrics such as recall, 1-precision, and F₁ score for inlier classification, SPROSAC demonstrates the best performance across the three datasets, with outlier rates exceeding 95%. Furthermore, we propose a coarse–fine PCR algorithm based on SPROSAC and ICP to address the issues of high initialization requirements, susceptibility to local optima, and low efficiency in traditional ICP algorithms. The experimental results of coarse–fine registration show that our algorithm provides initial values for the ICP, which can reduce the number of iterations of the ICP by 50%, 64.4%, and 57.4%.