Addressing the challenges posed by the substantial gap in point cloud data
collected from diverse sensors, achieving robust cross-source point cloud
registration becomes a formidable task. In response, we present a novel
framework for point cloud registration with broad applicability, suitable for
both homologous and cross-source registration scenarios. To tackle the issues
arising from different densities and distributions in cross-source point cloud
data, we introduce a feature representation based on spherical voxels.
Furthermore, addressing the challenge of numerous outliers and mismatches in
cross-source registration, we propose a hierarchical correspondence filtering
approach. This method progressively filters out mismatches, yielding a set of
high-quality correspondences. Our method exhibits versatile applicability and
excels in both traditional homologous registration and challenging cross-source
registration scenarios. Specifically, in homologous registration using the
3DMatch dataset, we achieve the highest registration recall of 95.1% and an
inlier ratio of 87.8%. In cross-source point cloud registration, our method
attains the best RR on the 3DCSR dataset, demonstrating a 9.3 percentage points
improvement. The code is available at this https URL

基于球形体素的特征表示和分层对应筛选的点云配准方法适用于同质和异质数据，能有效应对不同密度和分布的点云数据，滤出高质量对应点，实现了多领域点云配准。