QGORE: Quadratic-Time Guaranteed Outlier Removal for Point Cloud Registration

With the development of 3D matching technology, correspondence-based point cloud registration gains more attention. Unfortunately, 3D keypoint techniques inevitably produce a large number of outliers, i.e., outlier rate is often larger than 95%. Guaranteed outlier removal (GORE) Bustos and Chin has shown very good robustness to extreme outliers. However, the high computational cost (exponential in the worst case) largely limits its usages in practice. In this paper, we propose the first
$O(N^{2})$
time GORE method, called quadratic-time GORE (QGORE), which preserves the globally optimal solution while largely increases the efficiency. QGORE leverages a simple but effective voting idea via geometric consistency for upper bound estimation, which achieves almost the same tightness as the one in GORE. We also present a one-point RANSAC by exploring “rotation correspondence” for lower bound estimation, which largely reduces the number of iterations of traditional 3-point RANSAC. Further, we propose a
l$_{p}$p
-like adaptive estimator for optimization. Extensive experiments show that QGORE achieves the same robustness and optimality as GORE while being 1
$\sim$
2 orders faster. The source code will be made publicly available.