VRHCF: Cross-Source Point Cloud Registration via Voxel Representation
  and Hierarchical Correspondence Filtering
Guiyu Zhao Zewen Du Zhentao Guo Hongbin Ma
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 https://github.com/GuiyuZhao/VRHCF.