An ideal point cloud registration framework should have superior accuracy, acceptable
efficiency, and strong generalizability. However, this is highly challenging since existing …

Point cloud registration is essential for many applications. However, existing real datasets
require extremely tedious and costly annotations, yet may not provide accurate camera …

Point cloud registration is the process of aligning and merging multiple point clouds into a
same coordinate system. It has many applications in computer vision, robotics, 3D …

In this paper, we present a new method for the multiview registration of point cloud. Previous
multiview registration methods rely on exhaustive pairwise registration to construct a …

For point cloud registration (PCR), a matching matrix is critical. Unfortunately, the existing
approaches do not explicitly devise schemes to refine the matching matrix. Furthermore …

Real point cloud registration, involving homologous and cross-source 3D data, poses
significant challenges such as partial overlap, high noise, density disparities, and scale …

Domain adaptation is commonly employed in crowd counting to bridge the domain gaps
between different datasets. However, existing domain adaptation methods tend to focus on …

This study proposes a set of generic rules to revise existing neural networks for 3D point
cloud processing to rotation-equivariant quaternion neural networks (REQNNs), in order to …

This study introduces a novel framework, G3Reg, for fast and robust global registration of
LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we …

Point cloud registration is a fundamental technique in 3-D computer vision with applications
in graphics, autonomous driving, and robotics. However, registration tasks under …