We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult�…

Abstract Three-dimensional (3D) point cloud registration is a fundamental step for many 3D
modeling and mapping applications. Existing approaches are highly disparate in the data�…

The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing
attention with the fast development background of 3D sensor technologies. Different from the�…

As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to
seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration�…

In this paper, we present a second order spatial compatibility (SC^ 2) measure based
method for efficient and robust point cloud registration (PCR), called SC^ 2-PCR. Firstly, we�…

Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that�…

Rigid registration is a transformation estimation problem between two point clouds. The two
point clouds captured may partially overlap owing to different viewpoints and acquisition�…

Matching local features across images is a fundamental problem in computer vision.
Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching�…

Point cloud registration is the task of estimating the rigid transformation that aligns a pair of
point cloud fragments. We present an efficient and robust framework for pairwise registration�…

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