  Many types of 3D acquisition sensors have emerged in recent years and point
cloud has been widely used in many areas. Accurate and fast registration of
cross-source 3D point clouds from different sensors is an emerged research
problem in computer vision. This problem is extremely challenging because
cross-source point clouds contain a mixture of various variances, such as
density, partial overlap, large noise and outliers, viewpoint changing. In this
paper, an algorithm is proposed to align cross-source point clouds with both
high accuracy and high efficiency. There are two main contributions: firstly,
two components, the weak region affinity and pixel-wise refinement, are
proposed to maintain the global and local information of 3D point clouds. Then,
these two components are integrated into an iterative tensor-based registration
algorithm to solve the cross-source point cloud registration problem. We
conduct experiments on synthetic cross-source benchmark dataset and real
cross-source datasets. Comparison with six state-of-the-art methods, the
proposed method obtains both higher efficiency and accuracy.
