An overlap estimation guided feature metric approach for real point cloud registration

Abstract

Real point cloud registration, involving homologous and cross-source 3D data, poses significant challenges such as partial overlap, high noise, density disparities, and scale variations. In this paper, we introduce a framework called OEFM to guide transform estimation on the basis of feature metrics through overlap estimation with overlap region center weighting, which estimates transform parameters by minimizing feature projection error on overlap region features. Our proposed approach utilizes an overlap filtering module with a center-weighting mechanism to filter overlap points and feeds these points into a feature metric framework with a forward–backward transformation to estimate the transformation parameters. The rationale behind our approach is that only overlapping regions are useful for point cloud registration, while non-overlapping regions are anomalous. Furthermore, our proposed approach requires no correspondence search, making it robust to partial overlaps, large noise, density differences, and scale variations. We show that our approach achieves the highest registration recall for 3DMatch and 3DCSR in extensive experiments on both homologous and cross-source datasets.